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In this paper, we study the multi-target tracking problem of networked robotic systems (NRSs) within predefined time under a directed interaction topology considering disturbances and physical parameter uncertainties. On the basis of non-singular sliding-mode surface, a new predefined-time estimator-based hierarchical control algorithm is put forward so as to address the tracking problem under the case of multiple moving targets. Using the time base generator (TBG), a distributed estimator algorithm is designed to ensure that the followers can estimate different target states within a predefined-time. Certain sufficient conditions of predefined-time multi-target tracking of NRSs are presented using the Lyapunov stability and neighbor information interaction principle. In the end, numerical simulation experiments are offered to indicate the availability and correctness of the theoretical results. [ABSTRACT FROM AUTHOR]

This study aims to address the problem in tracking technology in which targeted cruising ships or submarines sailing near the water surface are tracked at low frame rates or with some frames missing in the video image, so that the tracked targets have a large gap between frames, leading to a decrease in tracking accuracy and inefficiency. Thus, in this study, we proposed a water surface dynamic multi-target tracking algorithm based on the fusion of YOLOv7 and DeepSORT. The algorithm first introduces the super-resolution reconstruction network. The network can eliminate the interference of clouds and waves in images to improve the quality of tracking target images and clarify the target characteristics in the image. Then, the shuffle attention module is introduced into YOLOv7 to enhance the feature extraction ability of the target features in the recognition network. Finally, Euclidean distance matching is introduced into the cascade matching of the DeepSORT algorithm to replace the distance matching of IOU to improve the target tracking accuracy. Simulation results showed that the algorithm proposed in this study has a good tracking effect, with an improvement of 9.4% in the improved YOLOv7 model relative to the mAP50-95 value and an improvement of 13.1% in the tracking accuracy in the DeepSORT tracking network compared with the SORT tracking accuracy. [ABSTRACT FROM AUTHOR]

In order to address the multi-target tracking problem of airborne passive bisstatic radar (APBR) in an unknown clutter environment, a robust adaptive labeled multi-Bernoulli (RA-LMB) filter is proposed. Firstly, a model for multi-target tracking problem is established on the basis of the LMB filter algorithm framework. Then, for the problems of unknown target newborn parameters, clutter parameters and target detection probability, the measurement-driven target newborn model and the online parameter estimation method based on the cardinally-balanced multi-target multi-Bernoulli estimator are proposed. Finally, considering the non-linearity of APBR measurements, the sequential Monte Carlo method is used to implement the proposed algorithm. The experimental results show that the proposed filter is able to estimate the multi- target trajectory using the APBR measurements, and the performance in the unknown clutter environment can be approximated to the generalized LMB filter with known clutter parameters, with better robustness. [ABSTRACT FROM AUTHOR]

Fry counting can play a pivotal role in the aquaculture industry. However, manual counting cannot fully meet the large-scale production in recent years, due to the inefficient, low precision, and even damage to the fry. In this study, a new fry counting was proposed using improved YOLOv8 and multi-target tracking. The objects were taken as the grass carp fry with the body lengths ranging from 20 to 50 mm. Firstly, the test platform was designed, according to the test requirements. Eight fry videos were then captured to extract and screen 961 valid images as the initial dataset. Then augmentation operations were performed on the data, including motion blur, brightness adjustment, noise increase, and image flipping. 2285 images dataset was divided into the training set and testing set in the ratio of 9:1. P2 small layer of target detection was then added into the YOLOv8, due to the small fry targets and high detection speed. While the GAM (Global Attention Mechanism) was introduced in front of the detection head, and the Inner-SIoU (Inner-SCYLLA- Intersection over Union) loss function was used to accelerate the convergence speed of the model for the better recognition accuracy of small and overlapping targets. The YOLOv8 detector was trained with the following settings: initial learning rate of 0.01, moments of 0.9, weight decay of 0.0005, batch size of 16, and 300 training rounds. The image processing computer was a 12th Gen Intel(R) Core (TM) i7-12700H 2.70 GHz, with an operating system of 64-bit Windows 11 system with 24 G of system memory. After that, a tracking and counting approach was realized for fry counting using BoT-SORT multi-target tracking with motion feature matching, eight-element Kalman filter, and camera motion compensation and discarding the appearance feature matching module. The tracking of fry targets was realized for the fry targets. Finally, the performance of the improved model was also evaluated, in terms of merits and performance. The ablation tests showed that the improved model was significantly promoted the counting performance, compared with the pre-improved model; Comparative test results showed that the average counting accuracy (ACP), average absolute error (MAE), and root mean square error (RMSE) were 97.16%, 3.27, and 5.16, respectively. The indexes of improved model were better than those of YOLOv5+DeepSORT, YOLOv8+DeepSORT, YOLOv8+StrongSORT, YOLOv8+ByteTrack, and YOLOv8+BoT-SORT; The comparative tests showed that the counting performance was negatively correlated with the speed of the fry through the shooting area. Once the speed was too high, the tracker was caused the insufficient inference speed, leading to a decline in the counting accuracy. The number of small fries were found in the video with the higher speed and accuracy than before. The finding can lay the strong foundation for the rapid and accurate counting of fry on the biomass estimation in factory aquaculture. [ABSTRACT FROM AUTHOR]

In intelligent traffic monitoring systems, the significant distance between cameras and their non-overlapping fields of view leads to several issues. These include incomplete tracking results from individual cameras, difficulty in matching targets across multiple cameras, and the complexity of inferring the global trajectory of a target. In response to the challenges above, a deep learning-based vehicle tracking algorithm called FairMOT-MCVT is proposed. This algorithm con-siders the vehicles' characteristics as rigid targets from a roadside perspective. Firstly, a Block-Efficient module is designed to enhance the network's ability to capture and characterize image features across different layers by integrating a multi-branch structure and depth-separable convolutions. Secondly, the Multi-scale Dilated Attention (MSDA) module is introduced to improve the feature extraction capability and computational efficiency by combining multi-scale feature fusion and attention mechanisms. Finally, a joint loss function is crafted to better distinguish between vehicles with similar appearances by combining the trajectory smoothing loss and velocity consistency loss, thereby considering both position and velocity continuity during the optimization process. The proposed method was evaluated on the public UA-DETRAC dataset, which comprises 1210 video sequences and over 140,000 frames captured under various weather and lighting conditions. The experimental results demonstrate that the FairMOT-MCVT algorithm significantly enhances multi-target tracking accuracy (MOTA) to 79.0, IDF1 to 84.5, and FPS to 29.03, surpassing the performance of previous algorithms. Additionally, this algorithm expands the detection range and reduces the deployment cost of roadside equipment, effectively meeting the practical application requirements. [ABSTRACT FROM AUTHOR]

Under space-based observation conditions, targets are subject to a large number of stars, clutter, false alarms, and other interferences, which can significantly impact the traditional Gaussian mixture probability hypothesis density (GM-PHD) filtering method, leading to tracking biases. To enhance the capability of the traditional GM-PHD method for multi-target tracking in space-based platform observation scenarios, in this article, we propose a GM-PHD algorithm based on spatio-temporal pipeline filtering and enhance the conventional spatio-temporal pipeline filtering method. The proposed algorithm incorporates two key enhancements: firstly, by adaptively adjusting the pipeline's central position through target state prediction, it ensures continuous target tracking while eliminating noise; secondly, by computing trajectory similarity to distinguish stars from targets, it effectively mitigates stellar interference in target tracking. The proposed algorithm realizes a more accurate estimation of the target by constructing a target state pipeline using the time series and correlating multiple frames of data to achieve a smaller optimal sub-pattern assignment (OSPA) distance and a higher tracking accuracy compared with the traditional algorithm. Through simulations and real-world data validation, the algorithm showcased its capability for multi-target tracking in a space-based context, outperforming traditional methods and effectively addressing the challenge of stellar interference in space-based multi-target tracking. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of South-Central Minzu University (Natural Science Edition) is the property of Journal of South-Central Minzu University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The proliferation of electronic countermeasure (ECM) technology has presented military radar with unprecedented challenges as it remains the primary method of battlefield situational awareness. In this paper, a joint antenna scheduling and power allocation (JASPA) scheme is put forward for multi-target tracking (MTT) in the distributed multiple-input multiple-output (D-MIMO) radar. Aiming at radar resource scheduling in the presence of range deception jamming (RDJ), the false target discriminator is designed based on the Cramer–Rao lower bound (CRLB) in terms of the spoofing range, and the predicted conditional CRLB (PC-CRLB) plays a role in evaluating tracking accuracy. The JASPA scheme integrates the quality of service (QoS) principle to develop an optimization model based on false target discrimination, with the objective of enhancing both the discrimination probability of false targets and the tracking accuracy of real targets concurrently. Since the optimal variables can be separated in constraints, a four-step optimization cycle (FSOC)-based algorithm is developed to solve the multidimensional non-convex problem. Numerical simulation results are provided to illustrate the effectiveness of the proposed JASPA scheme in dealing with MTT in the RDJ environment. [ABSTRACT FROM AUTHOR]

The activity level of newborn lambs is one of the most important indicators to assess the vitality of the lambs and the reproductive capacity of ewes. In this study, a novel multi-object tracking (named state vector-enhanced ByteTrack) was proposed to automatically calculate the vitality of newborn lambs. This innovative approach integrates advanced tracking algorithms to analyze lamb movement with high precision detection. According to the activity of lambs, the breeding performance of ewes was evaluated, and the lambs with low vitality were detected in time. Traditional multi-object tracking (i.e., ByteTrack) tends to cause identity switching, when the occlusions occurred. Furthermore, the state vectors that only contained the position and shape information were not sufficient to accurately distinguish between occluding and occluded lambs. Specifically, the higher confidence scores were received in the lambs that were occluding, while the occluded lambs received the lower confidence scores. The confidence information was used to better distinguish between overlapping newborn lambs, thereby improving tracking accuracy. Once the tracking target was lost, the linear Kalman filter was led to accumulate the errors in the predicted trajectory. The cubic spline interpolation was used to reconstruct the virtual trajectory during the target loss period. The state vector of the lost target's trajectory was then re-updated, according to the reconstructed virtual trajectory. The accuracy of the trajectory state vector was improved, and the cumulative error in the predicted trajectory is corrected. The state vector-enhanced ByteTrack was used to track the activities of newborn lambs, thereby obtaining the movement trajectory for each lamb. According to the movement trajectory, the displacement between adjacent frames was calculated for each lamb, and then summed to represent the activity level of the lamb. The performance of the state vectorenhanced ByteTrack was tested on the video dataset of lamb activity collected at the Jiangsu Haimen Goat Research and Development Center. The extensive testing phase ensured the reliability and accuracy of the tracking system under various conditions. The experimental results show that the proposed multi-target tracking achieved the remarkable performance on several key indexes. Specifically, the high-order tracking accuracy (HOTA) was 80.8%, the multi-target tracking accuracy (MOTA) was 86.1%, the multi-target tracking precision (MOTP) was 84.5%, and the recognition F1 score (IDF1) was 92.2%. The indexes were 2.7, 0.2, 2.3, and 3.9 percentage points higher than those of current mainstream methods, respectively. The state vector-enhanced ByteTrack demonstrates robustness under different birthing conditions, providing consistent and reliable tracking performance that can be effectively deployed in real-world farming scenarios. In summary, the state vector-enhanced ByteTrack algorithm innovatively uses the confidence information and track state vector to re-update, and realizes the stable tracking of newborn lambs. The scheme provides data and technical support for automatic calculation of lamb vitality and evaluation of ewe breeding performance. The practicability and effectiveness of the model were verified to monitor the animal health and reproductive performance. Ultimately, a great contribution was gained to better manage and select breeding stock, in order to enhance the productivity and sustainability in livestock farming. [ABSTRACT FROM AUTHOR]

In the field of target tracking, probability multiple hypothesis tracking (PMHT) algorithm, as a batch processing algorithm, has much less computation than the traditional multiple hypothesis tracking algorithm. Currently, the application of PMHT algorithm is limited by centralized processing. On the basis of the traditional algorithm, this study firstly derives the algorithm likelihood under sensor network to obtain the post-correlation parameter under multi-sensor algorithm, followed by hybrid consensus based on the consensus processing strategy, and finally the posteriori estimation of the target parameters is accomplished by using Kalman filtering. This study enables the PMHT algorithm to be applied to the fully distributed sensor network without fusion centers. The experimental results show that under different clutter densities, the distributed PMHT has more than 90% improvement in tracking error compared to the single-sensor algorithm. Distributed PMHT has close tracking performance and faster computation compared to centralized algorithms. [ABSTRACT FROM AUTHOR]

The challenge of multi-object tracking stands as a fundamental focus in computer vision research, finding widespread applications in areas such as public safety, transportation, autonomous vehicles, robotics, and other domains involving artificial intelligence. Given the intricate nature of natural scenes, the occurrence of object occlusion and semi-occlusion is commonplace in basic tracking tasks. These factors often result in challenges such as ID switching, object loss, detection errors, and misaligned bounding boxes, thereby significantly impacting the precision of multi-object tracking.This paper aims to address the aforementioned issues and proposes a novel multi-object tracker, incorporating Relative location mapping (RLM) and Target region density (TRD) modeling. The new tracker is more sensitive to differences in the spatial relationships between targets, allowing it to dynamically introduce low-scoring detection boxes into different regions based on the density of target regions in the image. This improves the accuracy of target tracking while avoiding the consumption of a significant amount of computational resources.Our research results indicate that when applying this method to state-of-the-art multi-object tracking approaches, the proposed model achieves improvements of 0.4 to 0.8 points in the HOTA and IDF1 metrics on the MOT17 and MOT20 datasets. This demonstrates the effectiveness of the proposed method in enhancing multi-object tracking performance. [ABSTRACT FROM AUTHOR]

This paper presents a novel hybrid optimization method to solve the resource allocation problem for multi-target multi-sensor tracking of drones. This hybrid approach, the Improved Prairie Dog Optimization Algorithm (IPDOA) with the Genetic Algorithm (GA), utilizes the strengths of both algorithms to improve the overall optimization performance. The goal is to select a set of sensors based on norms of weighted distances cost function. The norms are the Euclidean distance and the Mahalanobis distance between the drone location and the sensors. The second one depends on the predicted covariance of the tracker. The Extended Kalman Filter (EKF) is used for state estimation with proper clutter and detection models. Since we use Multi-objects to track, the Joint Probability Distribution Function (JPDA) estimates the best measurement values with a preset gating threshold. The goal is to find a sensor or minimum set of sensors that would be enough to generate high-quality tracking based on optimum resource allocation. In the experimentation simulated with Stone Soup, one radar among five radars is selected at every time step of 50-time steps for 200 tracks distributed over 20 different ground truths. The proposed IPDOA provided optimum solutions for this complex problem. The obtained solution is an optimum offline solution that is used to select one or more sensors for any future flights within the vicinity of the 5 radars. Environment and conditions are assumed to be similar in future drone flights within the radars' defined zone. The IPDOA performance was compared with the other 8 metaheuristic optimization algorithms and the testing showed its superiority over those techniques for solving this complex problem. The proposed simulated model can find the most relevant sensor(s) capable of generating the best quality tracks based on weighted distance criteria (Euclidean and Mahalanobis). That would cut down the cost of operating extra sensors and then it would be possible to move them to other vicinity. [ABSTRACT FROM AUTHOR]

Tracking multiple targets in the presence of unknown number of targets, missed detection, clutter, and noise is a challenging problem. To cope with this problem, a novel approach for generating the potential birth targets was developed, a mathematical model for multiple hypotheses was established, and an adaptive multi-hypothesis marginal Bayes filter is herein proposed in terms of the established mathematical model for multiple hypotheses and the novel birth approach. This filter delivers the existence probabilities of targets and their probability density functions. It uses multiple hypotheses to solve the data association problem to form the existence probabilities of targets and their probability density functions. To obviate the requirement for prior birth models, this filter uses the observations from two consecutive time steps to establish the birth models adaptively. Its tracking performance was tested by comparing it with other adaptive filters, showing that the proposed filter is robust, and it can obtain higher tracking accuracy than other filters. [ABSTRACT FROM AUTHOR]

Multi-Object Tracking, also known as Multi-Target Tracking, is an important area of computer vision with various applications in different domains. The advent of deep learning has had a profound impact on this field, forcing researchers to explore innovative avenues. Deep learning methods have become the cornerstone of today's state-of-the-art solutions, consistently delivering exceptional tracking results. However, the significant computational demands of deep learning models require powerful hardware resources that do not always match real-time tracking requirements, limiting their practical applicability in real-world scenarios. Thus, there is an imperative to strike a balance by merging robust deep learning strategies with conventional approaches to enable more accessible, cost-effective solutions that meet real-time requirements. This paper embarks on this endeavor by presenting a hybrid strategy for real-time multi-target tracking. It effectively combines a classical optical flow algorithm with a deep learning architecture tailored for human crowd tracking systems. This hybrid approach achieves a commendable balance between tracking accuracy and computational efficiency. The proposed architecture, subjected to extensive experimentation in various settings, demonstrated notable results, achieving a Mean Object Tracking Accuracy (MOTA) of 0.608. This level of performance placed it as the highest ranking solution on the MOT15 benchmark, surpassing the state-of-the-art benchmark of 0.549, and consistently ranked among the superior models on the MOT17 and MOT20 benchmarks. Additionally, the incorporation of the optical flow phase resulted in a substantial reduction in processing time, nearly halving the duration, while simultaneously maintaining accuracy levels comparable to established techniques. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Maritime Tactical Data System is a software system that collects track data from maritime sensors to compile and show on a sea map to provide maritime patrol vessels or maritime surveillance stations with situational awareness. Speed and precision in tracking multiple targets are crucial in achieving situational awareness. A multi-target tracking problem is NP-hard if it involves more than two sensors and a large amount of data since it generates many potential solutions that must be evaluated. Previous research has demonstrated that the Density-based Spatial Clustering of Applications with Noise (DBSCAN) method can perform track-to-track association with pretty good results; nevertheless, the density-reachable concept of DBSCAN poses a problem when two targets are within a distance less than the threshold. Another limitation is the inability of DBSCAN to associate tracks as soon as sensor track data is received. DBSCAN must run after all data has been collected in a database. In this paper, a novel track-to-track association method called Neighborhood Clustering Track Association and Fusion (NCTAF) is proposed to address the limitations of DBSCAN. According to the experiment results, NCTAF overcame the inaccurate cluster form generated by DBSCAN. The most remarkable result is that NCTAF performs track associations in an average of one second after receiving sensor track data involving three sensors, 4000 track data per sensor, and an update rate of 5-12 s per sensor. In contrast, DBSCAN required more than 10 min for the same scenario.

Petrochemical wastewater contains heavy metals and petroleum-based pollutants and is a major environmental and biological health hazard. Zebrafish are sensitive to water quality changes and can be used as biological indicators for water quality monitoring. The type, concentration, and toxicity of pollutants in the water can be inferred by observing zebrafish survival, behavior, activity, and other parameters. However, the traditional method of monitoring zebrafish toxicity-response behavior by manual observation and analysis is subjective, labor-intensive, and inefficient. Therefore, automating the monitoring and identification of zebrafish toxicity-response behavior using computer vision technology is an important and challenging research goal. The common methods of computer vision technology in zebrafish toxicity-response behavior monitoring and recognition can be divided into three steps: Foreground extraction, target tracking, and behavior analysis. However, there are problems such as sensitivity to light changes, inability to deal with occlusion and overlapping phenomena, and low efficiency. Therefore, the aim of this study was to improve efficiency and detection accuracy in complex situations such as fish shading for the automated and real-time identification of zebrafish toxicity-response behavior. In this study, four typical pollutants (zinc, chromium, lead, and phenol) in petrochemical tail water were selected to experimentally observe the swimming behavior of zebrafish at different concentrations and exposure times. A multi-target tracking technique based on YOLOv8+ Bytetrack was used to extract the characteristic values of zebrafish movements (average velocity, maximum velocity, minimum velocity, and average number of collisions). YOLOv8 is a deep learning-based end-to-end target detection algorithm that enables efficient and accurate target detection. Bytetrack is a multi-target tracking algorithm based on target detection that can achieve real-time target tracking coupled with the use of low-scoring frames in the tracking algorithm for secondary matching, which can effectively optimize the problem of switching IDs due to occlusion in the tracking process. The convolutional neural network Resnet was used to analyze the motion trajectory maps of zebrafish. The bounding box and confidence level output from the YOLOv8 model were inputted into the algorithm to obtain a unique ID and trajectory for each zebrafish. Finally, zebrafish features such as position, speed, number of wall touches, and trajectory were extracted based on the tracking results. The experimental results showed that the algorithm's tracking accuracy, missing rate, and detection time (per 300 frames) reached 90.26%, 16.33%, and 0.19 min, respectively, which represented a considerable improvement in detection time and accuracy over those of traditional target-detection methods. The tracking accuracy of manual labeling was up to 100%, and the monitoring time was 125.62 min, which was 661.16 times greater than that of the multi-target tracking method in this study. Moreover, the detection times of the threshold segmentation-based Kalman filter, SOTMOT-based multi-target tracking, and FairMOT-based multi-target tracking were 3.59, 0.41, and 0.37 min, respectively, representing 18.89-, 2.16-, and 1.95-fold increases over that of the proposed method, and the tracking accuracies were 67.09%, 88.52%, and 90.10%, which represented only 74.32%, 98.07%, and 99.82%, respectively, of the accuracy of this method. Moreover, the missing detection rates were 72.80%, 20.69%, and 26.45%, which were 4.46, 1.27, and 1.62 times greater than the missing detection rate of this method. This method outperforms other multi-target tracking methods (SOTMOT and Deepsort) regarding target-tracking accuracy and precision. Meanwhile, the proposed method can accurately identify the corresponding movement status and trajectory changes in zebrafish based on specific pollutants. An increase and then a decrease in velocity were observed in zebrafish exposed to zinc sulfate and lead acetate as compared to that of the control group. A significant difference (P < 0.05) exists between the effects of zinc sulfate and lead acetate on the increase in velocity of zebrafish at the beginning of the exposure. The velocity of zebrafish in the potassium dichromate-exposed group showed a fluctuating trend, with values slightly lower than those of the control group. In contrast, the proportion of abnormal trajectories was significantly higher (P < 0.05) than that in the other experimental groups. Under phenol exposure conditions, the velocity of zebrafish tended to fluctuate over a wide range, while the number of wall touches was significantly higher than that in the other experimental groups (P < 0.05). At the late stage of exposure, the velocity of zebrafish in zinc sulfate, lead acetate, and potassium dichromate exposure groups gradually stabilized. The velocity of zebrafish under zinc sulfate and lead acetate exposure conditions tended to decrease significantly. In the potassium dichromate group, the velocity of zebrafish under 1 and 2 TU phenol exposure increased sharply and then fluctuated within a certain range, and 4 TU phenol exposure resulted in partial mortality of zebrafish. In summary, the multi-target tracking method can quickly identify the type of pollutant to which zebrafish are exposed by setting thresholds for the speed, number of wall touches, and percentage of abnormal trajectories in zebrafish behavior. This method is simple, effective, performs accurate identification, and determines real-time responses, making it highly valuable for reference in fish toxicity-response behavior identification.

In this study, a joint power and time allocation algorithm in multi-radar system for cooperative target detection is proposed in multi-target search and tracking scenarios. The evaluation metrics for radar search and tracking performance are derived separately, based on signal detection theory and the Cramér-Rao lower bound. A joint optimization allocation model is developed for multi-aircraft radar systems geared towards cooperative detection. This model seeks to maximize radar operational performance as the optimization objective, which will be achieved under the constraint of predefined system resource. Parameters including radar selection, radiated power, and time in radar search and tracking missions are jointly optimized. By incorporating the interior point method and particle swarm algorithm, a three-step decomposition approach is employed to solving the optimization problem. The results reveal that the proposed algorithm compared to existing algorithms improves radar system search performance and tracking accuracy effectively while adhering to the predefined system resource constraint.

Appropriate and effective resource scheduling is the key to achieving the best performance for a space-based radar. Considering the resource scheduling problem of multi-target tracking in a space-based radar system, we establish a cost function that considers target threat, tracking accuracy, and Low Probability of Interception (LPI). Considering target uncertainty and constraints of the space-based platform and long-term expected cost, we establish a resource scheduling model based on the Partially Observable Markov Decision Process (POMDP) with multiple constraints. To transform and decompose the resource scheduling problem of multi-target tracking with multiple constraints into multiple unconstrained sub-problems, we use the Lagrangian relaxation method. To deal with the curse of dimensionality caused by the continuous state space, continuous action space and continuous observation space, we use the online POMDP algorithm based on the Monte Carlo Tree Search (MCTS) and partially observable Monte Carlo planning with observation widening algorithm. Finally, a non-myopic and fast resource scheduling algorithm with comprehensive performance indices for multi-target tracking in a space-based radar system is proposed. Simulation results show that the proposed algorithm, when compared with the existing scheduling algorithms, allocates resources more appropriately and shows better performance.

To improve the accuracy of multiple target tracking in the clutter environment, a new joint probabilistic data association (JPDA) algorithm based on variational Bayesian adaptive moment estimation is proposed. First, considering the existence of measurements, the posterior distribution of the target state in JPDA is composed of two parts of probability weighting, that is, the posterior distribution of the target state that the real measurement exists in the association gate and the posterior distribution of the target state that the real measurement does not exist in the association gate. By combining the conjugate properties of the prior and posterior distributions, the prior distributions of the target state in the two cases are classified to provide more accurate a priori information to filter, so as to improve the accuracy of data association. Second, considering the coupling effect between state estimation and data association process, combined with variational Bayesian inference, the problem of minimizing Kullback–Leibler divergence is transformed into the problem of maximizing the evidence lower bound, thereby effectively measuring the distance between the posterior distribution of target state estimation and the real posterior distribution, so as to improve the accuracy of data association again from the perspective of optimizing nonlinear filter. Finally, the adaptive momentum estimation strategy is introduced to iteratively solve the variable distribution that meets the maximization of the evidence lower bound, and the optimization of the posterior distribution of the target state is completed. Theoretical derivation and simulation experiments are conducted to verify the feasibility and effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The increasing popularity of vehicles has led to traffic congestion and frequent traffic accidents. Intelligent transportation technology is an effective solution to this problem. In order to improve the accuracy and effectiveness of vehicle detection and tracking, this paper combined the improved YOLOv5s model with the optimized DeepSORT tracking algorithm to detect and track vehicles on traffic roads. Firstly, in the detection model of YOLOv5s, the Attention-based Intra-scale Feature Interaction (AIFI) module is introduced to detect vehicles more quickly and accurately. Secondly, the Kalman filtering (KF) algorithm of DeepSORT is optimized to improve the accuracy of predictions of the vehicle state by using the width to replace the length-to-width ratio of the vehicle prediction box in the original KF algorithm. Finally, in the re-recognition network of DeepSORT, the original Convolutional Neural Network (CNN) model is replaced by an improved ResNet36 as the backbone network for feature extraction. The experimental results show that, compared with the original algorithm, when examining the performance of the improved algorithm in terms of target detection, the recall rate, average accuracy (mAP), and detection speed, are increased by 7.7%, 15.5%, and 14.2%, respectively; in terms of multi-object tracking performance, such as multi-object tracking precision (MOTP) and multi-object tracking accuracy (MOTA), improvements of 14.84% and 9.62%, respectively, are obtained and the total number of times a trajectory is fragmented (Frag) is reduced by 32.52%.These results indicate that the proposed algorithm can meet the requirements of accuracy, real-time detection, and stable vehicle detection and tracking on traffic roads. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this study, a joint power and time allocation algorithm in multi-radar system for cooperative target detection is proposed in multi-target search and tracking scenarios. The evaluation metrics for radar search and tracking performance are derived separately, based on signal detection theory and the Cramér-Rao lower bound. A joint optimization allocation model is developed for multi-aircraft radar systems geared towards cooperative detection. This model seeks to maximize radar operational performance as the optimization objective, which will be achieved under the constraint of predefined system resource. Parameters including radar selection, radiated power, and time in radar search and tracking missions are jointly optimized. By incorporating the interior point method and particle swarm algorithm, a three-step decomposition approach is employed to solving the optimization problem. The results reveal that the proposed algorithm compared to existing algorithms improves radar system search performance and tracking accuracy effectively while adhering to the predefined system resource constraint. [ABSTRACT FROM AUTHOR]

Combining the traffic light information to guide the speed of vehicles and reducing the number of starts and stops of vehicles can effectively reduce exhaust emissions and alleviate pollution problems caused by them. Aiming at the acquisition of traffic light transition time, this paper proposed a traffic light detection method based on network flow tracking. It introduced auxiliary traffic light class in the dataset for training, and correlated the detection results of this class in the video sequence as a tracklet, then modeled the multi-object tracking task by the tracklet. Specifically,this method converted the multi-target tracking task into the minimum cost flow optimization task. This method established the minimum cost flow network with the tracklet as the node, and proposed a cost construction method suitable for traffic lights, then obtained multiple traces of the auxiliary traffic light in the video sequence by solving the shortest path algorithm. Based on the solved trajectory results and image classification technology, it finally improved the performance of traffic light detection. Compared with the comparison algorithms, the proposed method greatly improved the tracking performance, and increased the mAP of the small target traffic light detection response to 94.35%.Experimental results show that the network flow modeling method can greatly improve the tracking effect of the auxiliary traffic light. Combined with the tracking results, it greatly improves the detection accuracy of the small target traffic light in the video sequence and effectively determines the transition time of traffic light status. [ABSTRACT FROM AUTHOR]

Object tracking technology based on image processing has made great progress recently. Based on the track-by-detection framework, the tracking algorithms are often combined with deep neural networks to perform online target tracking. However, existing motion models assume linearity and are sensitive to sudden changes in trajectories due to occlusion, overlap, or other detection issues. In this paper, we modified the existing object tracking algorithms and introduced a strong tracking filter (STF) module to the track-by-detection framework for solving the sudden change problem of the target. The STF module is designed to have a strong ability to track sudden changes by orthogonalizing the residual sequence. When the trajectory of the target is stable, the STF module returns to the inactive state, behaving similarly to tracking algorithms that follow conventional linear models. Experimental results on a public pedestrian tracking dataset show that the proposed method improves tracking performance on various metrics, including the ability to rematch missed trajectories. Moreover, compared with existing algorithms, the proposed method exhibits strong stability under noisy conditions. [ABSTRACT FROM AUTHOR]

In recent years, the Poisson multi-Bernoulli mixture (PMBM) filter has been established among the state-of-the art methods in target tracking. We present a method for including target-provided measurements in said filter, both when using it to track extended objects and point targets. We use messages from the Automatic identification system as an example of target-provided measurements, and radar and LiDAR as examples of exteroceptive sensors. In the point target case, we utilize several different kinematic models in parallel through the interacting multiple models framework, and compare the presented method to several common trackers and other PMBM filter configurations. The results show that our method outperforms similar methods when target-provided measurements are available. The point target variant is also shown to work in a closed-loop collision avoidance experiment in a maritime environment, demonstrating its feasibility for use in real-world applications. For the extended object tracking case, we expand upon the Gaussian process PMBM filter. The extended object method is evaluated on both simulated and experimental data, and is shown to improve the tracking performance when including target-provided measurements in comparison to when it only uses exteroceptive measurements.

Abstract In this article, a simple, yet effective, Bayesian scheme for tracks maintenance, promotion, and deletion in drone surveillance radar is presented. It enables the simultaneous tracking of the target body and micro‐Doppler components that originate from the motion of rotors (if any) onboard an unmanned air system. This not only delivers more accurate multi‐target tracking, but also substantially improves the radar automatic target classification capability (e.g. discriminating between drone and non‐drone targets). Challenging and diverse real staring radar datasets are used here to demonstrate the efficacy and benefits of the proposed track management approach.

This study aims to address the problem in tracking technology in which targeted cruising ships or submarines sailing near the water surface are tracked at low frame rates or with some frames missing in the video image, so that the tracked targets have a large gap between frames, leading to a decrease in tracking accuracy and inefficiency. Thus, in this study, we proposed a water surface dynamic multi-target tracking algorithm based on the fusion of YOLOv7 and DeepSORT. The algorithm first introduces the super-resolution reconstruction network. The network can eliminate the interference of clouds and waves in images to improve the quality of tracking target images and clarify the target characteristics in the image. Then, the shuffle attention module is introduced into YOLOv7 to enhance the feature extraction ability of the target features in the recognition network. Finally, Euclidean distance matching is introduced into the cascade matching of the DeepSORT algorithm to replace the distance matching of IOU to improve the target tracking accuracy. Simulation results showed that the algorithm proposed in this study has a good tracking effect, with an improvement of 9.4% in the improved YOLOv7 model relative to the mAP50-95 value and an improvement of 13.1% in the tracking accuracy in the DeepSORT tracking network compared with the SORT tracking accuracy.

In intelligent traffic monitoring systems, the significant distance between cameras and their non-overlapping fields of view leads to several issues. These include incomplete tracking results from individual cameras, difficulty in matching targets across multiple cameras, and the complexity of inferring the global trajectory of a target. In response to the challenges above, a deep learning-based vehicle tracking algorithm called FairMOT-MCVT is proposed. This algorithm con-siders the vehicles’ characteristics as rigid targets from a roadside perspective. Firstly, a Block-Efficient module is designed to enhance the network’s ability to capture and characterize image features across different layers by integrating a multi-branch structure and depth-separable convolutions. Secondly, the Multi-scale Dilated Attention (MSDA) module is introduced to improve the feature extraction capability and computational efficiency by combining multi-scale feature fusion and attention mechanisms. Finally, a joint loss function is crafted to better distinguish between vehicles with similar appearances by combining the trajectory smoothing loss and velocity consistency loss, thereby considering both position and velocity continuity during the optimization process. The proposed method was evaluated on the public UA-DETRAC dataset, which comprises 1210 video sequences and over 140,000 frames captured under various weather and lighting conditions. The experimental results demonstrate that the FairMOT-MCVT algorithm significantly enhances multi-target tracking accuracy (MOTA) to 79.0, IDF1 to 84.5, and FPS to 29.03, surpassing the performance of previous algorithms. Additionally, this algorithm expands the detection range and reduces the deployment cost of roadside equipment, effectively meeting the practical application requirements.

Under space-based observation conditions, targets are subject to a large number of stars, clutter, false alarms, and other interferences, which can significantly impact the traditional Gaussian mixture probability hypothesis density (GM-PHD) filtering method, leading to tracking biases. To enhance the capability of the traditional GM-PHD method for multi-target tracking in space-based platform observation scenarios, in this article, we propose a GM-PHD algorithm based on spatio-temporal pipeline filtering and enhance the conventional spatio-temporal pipeline filtering method. The proposed algorithm incorporates two key enhancements: firstly, by adaptively adjusting the pipeline’s central position through target state prediction, it ensures continuous target tracking while eliminating noise; secondly, by computing trajectory similarity to distinguish stars from targets, it effectively mitigates stellar interference in target tracking. The proposed algorithm realizes a more accurate estimation of the target by constructing a target state pipeline using the time series and correlating multiple frames of data to achieve a smaller optimal sub-pattern assignment (OSPA) distance and a higher tracking accuracy compared with the traditional algorithm. Through simulations and real-world data validation, the algorithm showcased its capability for multi-target tracking in a space-based context, outperforming traditional methods and effectively addressing the challenge of stellar interference in space-based multi-target tracking.

In recent years, multi-target tracking algorithms have been developed rapidly. However, in multi-target tracking, mutual occlusion and cross between targets and sudden disappearance and reappearance of targets in videos can easily occur, which could only result in missed detection, false detection, and wrong ID switching. To address the above problems, the CenterNet attention DeepSORT algorithm (CNA-DeepSORT) proposed in this paper incorporates a CenterNet network with channel attention mechanism in the original detection part of the DeepSORT algorithm instead of Faster R-CNN, and designs a multi-scale feature extraction module with the DeepSORT algorithm in the multi-scale feature extraction module and designed a pedestrian recognition network combined with the DeepSORT algorithm. These improvements lead to a 3.7% improvement in MOTA metric, 1.6% improvement in MOTP metric, 238 fewer false ID switches, 2627 fewer FP metrics, 3943 fewer FN metrics, a decrease in run speed, and a 4 Hz reduction in frame rate compared to the original DeepSORT algorithm. improved by 3. 7, and there is some improvement in handling the occlusion problem of multi-target tracking, and the false and missed detection of targets during ID switching is reduced. [ABSTRACT FROM AUTHOR]

In this article, a simple, yet effective, Bayesian scheme for tracks maintenance, promotion, and deletion in drone surveillance radar is presented. It enables the simultaneous tracking of the target body and micro‐Doppler components that originate from the motion of rotors (if any) onboard an unmanned air system. This not only delivers more accurate multi‐target tracking, but also substantially improves the radar automatic target classification capability (e.g. discriminating between drone and non‐drone targets). Challenging and diverse real staring radar datasets are used here to demonstrate the efficacy and benefits of the proposed track management approach. [ABSTRACT FROM AUTHOR]

Copyright of Electric Drive for Locomotives is the property of Electric Drive for Locomotives Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Forests established through afforestation are one of the most precious natural resources, especially in harsh and desert-biased conditions. Trees are often exposed to various threats that need to be addressed. Some of the threats are igniting fires, illegal lumberjacking, hunting, using, and crossing prohibited areas, etc. This article delves into the combination of advanced technologies, such as radars, thermal imaging, remote sensing, artificial intelligence, and biomass monitoring systems, in the field of forestry and natural resource security. By examining the parametric assurance technologies described in this paper, the potentials of real-time monitoring, early detection of threats, and rapid response capabilities are examined, which significantly improves the efficiency of forest protection efforts. This article deals with the presentation of advanced algorithms that include radar, thermal cameras, and artificial intelligence, which enable the automatic identification and classification of potential threats with a false alarm rate (FAR) as low as possible. The article presents a systemic solution that optimizes the answer for a parametric security system that is required to work in a complex environment with multiple triggers that can cause false alarms. In addition to this, a presented system is required to be easy to assemble and have the ability to integrate into natural areas and serve as a vulnerable aid in nature as much as possible. In conclusion, this study highlights the transformative potential of security applications in improving forest and natural reserve security while taking into account the complexity of the environment. [ABSTRACT FROM AUTHOR]

The article presents a novel Convex Uncertain Voronoi Diagram (CUV) for safe multi-robot multi-target tracking under localization uncertainty. Topics include the CUV diagram, distributed multi-target tracking, and coverage control algorithms, addressing challenges in uncertainty, localization, and collision avoidance in multi-robot systems.

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)