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The task of multi-object tracking of fish fry poses significant challenges, as the majority of the fish fry individuals exhibit highly similar appearances, and the feature distinctions between individual targets are not readily apparent. Consequently, fish tracking algorithms relying primarily on appearance-based features for data association often suffer from low accuracy and poor robustness. To address the challenges inherent in multi-object tracking of fish fry, this study presents an improved DeepSort-based algorithm, dubbed PLCFishMOT, designed specifically for enhanced performance in this domain. Furthermore, the fish fry trajectories may exhibit nonlinear characteristics due to external perturbations. To address this, the original Kalman filtering method has been replaced with a particle filtering approach, which is more suitable for handling nonlinear and non-Gaussian problems. This modification serves to enhance the accuracy of the trajectory prediction process. To further bolster the accuracy of the data association process, the proposed framework incorporates a large separable kernel attention mechanism into the original feature extraction network. This mechanism leverages convolutional kernels of varying sizes to extract target features with differing receptive field dimensions, thereby enhancing the overall effectiveness of the feature representation. The proposed approach effectively addresses the challenge of incorrect ID assignment, which can arise due to the close parallel swimming patterns exhibited by the fish fry. This is achieved by leveraging the cosine angle value between the fry detection frame and the trajectory frame as a discriminating factor. The experimental evaluation of the proposed algorithm on an open-source video dataset demonstrates its strong performance, with the algorithm achieving an IDF1 score of 75.8%, a MOTA score of 98.1%, and IDs is 10, respectively. Furthermore, to assess the generalization capabilities of the proposed approach, validation experiments were conducted using a fish fry video dataset captured in real-world aquaculture scenarios. The experimental results demonstrate that the PLCFishMOT algorithm achieves the best tracking performance compared to other advanced multi-object tracking algorithms. [ABSTRACT FROM AUTHOR]

Pedestrian multiple object tracking targets to track multiple pedestrian instances in real-time. Recently, the methods based on joint detection and embedding have improved performance by sharing task features. However, it has two obvious shortcomings: inconsistent task information and ambiguous neighbor instance overlap. Hence, the branch tasks information gap and instances information gap need to be carefully addressed. In this paper, IGTracker is proposed as a novel online tracking framework, which bridges different branch task optimization requirements from the perspective of task-specific information gaps and nearest instance information gaps. Firstly, to alleviate the competitive conflict between subtasks, we propose a shuffle involution decoupling (SID) module, which constructs task-specific features by focusing on local interaction information and global long-range dependencies of key points. Secondly, the nearest neighbor information enhancement (NNIE) strategy is proposed to reduce the ambiguity between similar instances by leveraging the adjacency key point information gap. As a bonus, our proposed IGTracker achieves competitive performance compared to various existing methods on the MOTChallenge benchmarks. [ABSTRACT FROM AUTHOR]

This study aimed to assess the impact of biological sex, concussion history, and type of sport on the baseline NeuroTracker performance, a test/train three-dimensional multiple object tracking paradigm used in sport contexts, in university level varsity athletes. A total of 136 university level varsity athletes participating in male ice hockey, male or female soccer, female volleyball, and mixed biological sex cheerleading underwent preseason NeuroTracker baseline assessments. Significant differences in NeuroTracker performance were observed based on biological sex (p < 0.01) and type of sport played (p < 0.05). Male athletes and hockey players demonstrated higher NeuroTracker performance compared to their counterparts. However, no significant differences were found in NeuroTracker performance based on the history of concussion. Thus, factors such as biological sex and type of sport played may influence baseline NeuroTracker performance. [ABSTRACT FROM AUTHOR]

Simple Summary: The honey bee (Apis mellifera) is of great significance to both the ecological environment and human society. Systematic monitoring of honey bee activities is a useful method for determining the colony condition and can facilitate pre-emptive measures against detrimental conditions. It is relatively easy to digitally monitor and analyze the incoming and outgoing actions of bees in current research. Cameras within beehives were used to collect the activity data of bees. Our work mainly involved the application and evaluation of algorithms and methods. Object detection algorithms and multiple object tracking algorithms were used to track the honey bees' incoming and outgoing actions. Based on the object detection algorithm and the multiple object tracking algorithm, in-and-out counting methods (the single-line method and the box method) were designed. After three levels of evaluation, the results showed that the in-and-out counting model consists of YOLOv8m, OC-SORT, and the box method, which achieved the best performance, and the model can be used to analyze the activity and colony condition. The bees' in-and-out activity is an essential indicator of bee behavior and can be useful to determine the colony condition. The traditional counting of bees' in-and-out activity mainly relies on manual work, which is time-consuming and inefficient. Therefore, there is a need to devise some more efficient alternative methods. For this purpose, the present study was conducted. This study proposed a bee in-and-out activity counting method based on video detection, including object detection, multiple object tracking (MOT), and an in-and-out activity counting algorithm. Images and video stream data were captured using a camera (Huiboshi model X20, Shenzhen Huiboshi Technology, Shenzhen, China) in a smart beehive. Two bee in-and-out counting methods, single-line and box methods, were designed. After three levels of evaluation, the results showed that the best counting model consists of YOLOv8m, OC-SORT, and the box method, which achieved F1in of 91.49% and F1out of 89.08%. The model can track and count honey bees in a complex environment and can be used to analyze their activity and colony condition. [ABSTRACT FROM AUTHOR]

The illumination of images can significantly impact computer-vision applications such as image classification, multiple object detection, and tracking, leading to a significant decline in detection and tracking accuracy. Recent advancements in deep learning techniques have been applied to Low-Light Image Enhancement (LLIE) to combat this issue. Retinex theory-based methods following a decomposition-adjustment pipeline for LLIE have performed well in various aspects. Despite their success, current research on Retinex-based deep learning still needs to improve in terms of optimization techniques and complicated convolution connections, which can be computationally intensive for end-device deployment. We propose an Optimized Retinex-Based CNN (OptiRet-Net) deep learning framework to address these challenges for the LLIE problem. Our results demonstrate that the proposed method outperforms existing state-of-the-art models in terms of full reference metrics with a PSNR of 21.87, SSIM of 0.80, LPIPS of 0.16, and zero reference metrics with a NIQE of 3.4 and PIQE of 56.6. Additionally, we validate our approach using a comprehensive evaluation comprising five datasets and nine prior methods. Furthermore, we assess the efficacy of our proposed model combining low-light multiple object tracking applications using YOLOX and ByteTrack in Versatile Video Coding (VVC/H.266) across various quantization parameters. Our findings reveal that LLIE-enhanced frames surpass their tracking results with a MOTA of 80.6% and a remarkable precision rate of 96%. Our model also achieves minimal file sizes by effectively compressing the enhanced low-light images while maintaining their quality, making it suitable for resource-constrained environments where storage or bandwidth limitations are a concern. [ABSTRACT FROM AUTHOR]

In this study, an improved stable multi-object simple online and real-time tracking (StableSORT) algorithm that was specifically designed for maritime environments was proposed to address challenges such as camera instability and irregular object motion. Specifically, StableSORT integrates a buffered IoU (B-IoU) and an observation-adaptive Kalman filter (OAKF) into the StrongSORT framework to improve tracking accuracy and robustness. A dataset was collected along the southern coast of Korea using a small autonomous surface vehicle to capture real-world maritime conditions. On this dataset, StableSORT achieved a 2.7% improvement in HOTA, 4.9% in AssA, and 2.6% in IDF1 compared to StrongSORT, and it significantly outperformed ByteTrack and OC-SORT by 84% and 69% in HOTA, respectively. These results underscore StableSORT's ability to maintain identity consistency and enhance tracking performance under challenging maritime conditions. The ablation studies further validated the contributions of the B-IoU and OAKF modules in maintaining identity consistency and tracking accuracy under challenging maritime conditions. [ABSTRACT FROM AUTHOR]

In order to solve the problems of low accuracy and poor real-time performance of existing target tracking algorithms in the complex environment of coal mines, a YOLO-FasterNet+ByteTrack coal mine personnel tracking algorithm was proposed based on the Tracking by Detection (TBD) paradigm. Firstly, the FasterNet-Block feature extraction module was constructed to improve the Backbone of YOLOv7 and improve the real-time performance of the object detection stage. Then, the CBAM attention mechanism was introduced into Neck to improve the feature perception ability of the model in complex scenes. Then, Soft-NMS is introduced in the decoding stage of object detection to optimize the detection accuracy of the model in personnel overlapping scenario. Finally, in the target tracking stage, a multi-target motion feature prediction mechanism fused with GRU and Kalman filter was designed to solve the problem of target ID flipping caused by personnel overlap and occlusion, which effectively improved the accuracy of coal mine personnel tracking. Experimental results show that the average accuracy of YOLO-FasterNet is increased by 3.6% and the detection speed is increased by 8.2FPS compared with YOLOv7 on the coal mine personnel dataset, and the MOTA value of the proposed target tracking algorithm is increased by 1.7% and the IDSW is reduced by 149 times compared with ByteTrack on the custom tracking dataset GBMOT.

Abstract The reading ability of English readers has been shown to correlate with psychophysical measurements of dynamic visual information processing. This study investigated the relationship between reading ability and dynamic visual information processing in healthy adult native Japanese readers (n = 46). Reading ability was assessed using three different tests: the Japanese Adult Reading Test (JART), transposed-letter detection task, and oral reading. Principal component analysis was performed on the scores on the three reading tests to quantify reading ability. Psychophysical thresholds were measured for contrast detection and speed discrimination with a drifting grating stimulus as well as for tracking two targets among concentrically revolving objects, providing an upper speed limit for attentional tracking. Simple correlation analysis revealed that one of the principal components correlated with the tracking speed limit. In addition, another principal component correlated with the speed-discrimination threshold, which is consistent with previous findings in English readers. These results suggest that Japanese reading ability involves at least two different processes, each sharing underlying mechanisms with visual motion and attentional processing.

The reading ability of English readers has been shown to correlate with psychophysical measurements of dynamic visual information processing. This study investigated the relationship between reading ability and dynamic visual information processing in healthy adult native Japanese readers (n = 46). Reading ability was assessed using three different tests: the Japanese Adult Reading Test (JART), transposed-letter detection task, and oral reading. Principal component analysis was performed on the scores on the three reading tests to quantify reading ability. Psychophysical thresholds were measured for contrast detection and speed discrimination with a drifting grating stimulus as well as for tracking two targets among concentrically revolving objects, providing an upper speed limit for attentional tracking. Simple correlation analysis revealed that one of the principal components correlated with the tracking speed limit. In addition, another principal component correlated with the speed-discrimination threshold, which is consistent with previous findings in English readers. These results suggest that Japanese reading ability involves at least two different processes, each sharing underlying mechanisms with visual motion and attentional processing. [ABSTRACT FROM AUTHOR]

Aerial images are increasingly adopted and widely used in various research areas. In marine mammal studies, these imagery surveys serve multiple purposes: determining population size, mapping migration routes, and gaining behavioral insights. A single aerial scan using a drone yields a wealth of data, but processing it requires significant human effort. Our research demonstrates that deep learning models can significantly reduce human effort. They are not only able to detect marine mammals but also track their behavior using continuous aerial (video) footage. By distinguishing between different age classes, these algorithms can inform studies on population biology, ontogeny, and adult-calf relationships. To detect beluga whales from imagery footage, we trained the YOLOv7 model on a proprietary dataset of aerial footage of beluga whales. The deep learning model achieved impressive results with the following precision and recall scores: beluga adult = 92%—92%, beluga calf = 94%—89%. To track the detected beluga whales, we implemented the deep Simple Online and Realtime Tracking (SORT) algorithm. Unfortunately, the performance of the deep SORT algorithm was disappointing, with Multiple Object Tracking Accuracy (MOTA) scores ranging from 27% to 48%. An analysis revealed that the low tracking accuracy resulted from identity switching; that is, an identical beluga whale was given two IDs in two different frames. To overcome the problem of identity switching, a new post-processing algorithm was implemented, significantly improving MOTA to approximately 70%. The main contribution of this research is providing a system that accurately detects and tracks features of beluga whales, both adults and calves, from aerial footage. Additionally, this system can be customized to identify and analyze other marine mammal species by fine-tuning the model with annotated data. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics 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 complex urban road conditions, stationary or illegally parked vehicles present a considerable risk to the overall traffic system. In safety-critical applications like autonomous driving, the detection of stopped vehicles is of utmost importance. Previous methods for detecting stopped vehicles have been designed for stationary viewpoints, such as security cameras, which consistently monitor fixed locations. However, these methods for detecting stopped vehicles based on stationary views cannot address blind spots and are not applicable from driving vehicles. To address these limitations, we propose a novel deep learning-based framework for detecting stopped vehicles in dynamic environments, particularly those recorded by dashcams. The proposed framework integrates a deep learning-based object detector and tracker, along with movement estimation using the dense optical flow method. We also introduced additional centerline detection and inter-vehicle distance measurement. The experimental results demonstrate that the proposed framework can effectively identify stopped vehicles under real-world road conditions. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Donghua University (Natural Science Edition) is the property of Journal of Donghua University (Natural Science) 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.)

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications 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.)

Multi-object tracking tasks aim to assign unique trajectory codes to targets in video frames. Most detection-based tracking methods use Kalman filtering algorithms for trajectory prediction, directly utilizing associated target features for trajectory updates. However, this approach often fails, with camera jitter and transient target loss in real-world scenarios. This paper rethinks state prediction and fusion based on target temporal features to address these issues and proposes the SimpleTrackV2 algorithm, building on the previously designed SimpleTrack. Firstly, to address the poor prediction performance of linear motion models in complex scenes, we designed a target state prediction algorithm called LSTM-MP, based on long short-term memory (LSTM). This algorithm encodes the target's historical motion information using LSTM and decodes it with a multilayer perceptron (MLP) to achieve target state prediction. Secondly, to mitigate the effect of occlusion on target state saliency, we designed a spatiotemporal attention-based target appearance feature fusion (TSA-FF) target state fusion algorithm based on the attention mechanism. TSA-FF calculates adaptive fusion coefficients to enhance target state fusion, thereby improving the accuracy of subsequent data association. To demonstrate the effectiveness of the proposed method, we compared SimpleTrackV2 with the baseline model SimpleTrack on the MOT17 dataset. We also conducted ablation experiments on TSA-FF and LSTM-MP for SimpleTrackV2, exploring the optimal number of fusion frames and the impact of different loss functions on model performance. The experimental results show that SimpleTrackV2 handles camera jitter and target occlusion better, achieving improvements of 1.6%, 3.2%, and 6.1% in MOTA, IDF1, and HOTA, respectively, compared to the SimpleTrack algorithm. [ABSTRACT FROM AUTHOR]

Multiple object tracking (MOT), as a typical application scenario of computer vision, has attracted significant attention from both academic and industrial communities. With its rapid development, MOT has becomes an hot topic. However, maintaining robust MOT in complex scenarios still faces significant challenges, such as irregular motion patterns, similar appearances, and frequent occlusions. Based on an extensive investigation into the state-of-the-art MOT, this survey has made the following efforts: 1) listing down preceding MOT approaches and current classifications; 2) surveying the MOT metrics and benchmark databases; 3) evaluating the MOT approaches frequently employed; 4) discussing the main challenges for MOT; and 5) putting forward potential directions for the development of future MOT approaches. By doing so, it strives to provide a systematic and comprehensive overview of existing MOT methods from SDE to TBA perspectives, thereby promoting further research into this emerging and important field. [ABSTRACT FROM AUTHOR]

BACKGROUND: Visual attention is critical in team sports, and multiple object tracking (MOT) task is a well-established experimental method for assessing it. This study aims to use a visual tracking task based on a soccer game video to compare the impact of different numbers of targets on the visual tracking performance of soccer players and that of non-soccer players. METHODS: 14 Chinese female soccer players (average age: 20.2±1.6 years) and 20 Chinese female non-soccer players (average age: 20.3±1.4 years) were selected to participate in the video-based MOT task with varying attentional load (four, six, or eight targets). This study examined the difference of dynamic visual attention features between female soccer players and non-players by changing the number of targets. RESULTS: A significant main effect of target number on tracking accuracy was identified, with accuracy decreasing as the number of targets increased (p < 0.001). Additionally, group differences were significant (p < 0.001), with female soccer players demonstrating superior accuracy compared to non-players. Furthermore, there was a significant interaction effect between group and target number (p < 0.05), indicating that female soccer players showed better tracking performance compared to non-players across various target quantities (i.e., 4, 6, and 8 targets). Specifically, within the group of female soccer players, tracking accuracy for 4 targets was significantly higher than for 6 and 8 targets (p < 0.05), yet no significant difference was observed between the tracking accuracies for 6 and 8 targets (p > 0.05). DISCUSSION: This study examined MOT for the first time using a video-based assessment method. Overall, the results suggest that video-based MOT is a sensitive measure to assess the visual tracking ability of female soccer players. In addition, the effect of expertise in female soccer games was found transferable to attention tasks related to other types of sports games. In order to provide better suggestions on performance in sports games, future research can adopt a more realistic game environment and incorporate motor-cognitive tasks. [ABSTRACT FROM AUTHOR]

This article systematically evaluates and models how brightness affects performance in Multiple Object Tracking (MOT) within screen-based environments. MOT proficiency is essential across various fields, and comprehending the elements that impact MOT performance holds significance for occupational health and safety. While previous studies have scrutinized the influence of brightness on object recognition, its repercussions on MOT performance in screen-based environments remain comparatively less comprehended. This research aims to bridge this gap by delving into the distinct and combined impacts of brightness-related factors on MOT performance. Additionally, it seeks to construct a computational model that can forecast MOT performance across diverse brightness conditions. The outcomes of this study will offer valuable insights into core psychological processes, thereby steering the development of more efficient visual displays to enhance occupational health and safety. Our findings revealed a significant correlation between brightness levels and MOT performance, with optimal tracking observed at medium brightness levels. Additionally, complex object motion patterns were found to exacerbate the challenges of tracking in low brightness settings. These insights have direct implications for screen-based interfaces, suggesting the need for adaptive brightness settings based on the content's complexity and the user's task. [ABSTRACT FROM AUTHOR]

Although rotifers (Brachionus plicatilis sp. complex) are an important first feed source in marine fish aquaculture, their management is quite time-consuming because their populations and movements need to be monitored daily. This management is still performed manually, and automation is required. If we could make good use of the recent breakthroughs in deep learning, the automating a rotifer culture system could be realized. We propose a deep learning framework for detecting and tracking rotifers as a basis for such automation and carefully verify its accuracy. Experimental results showed that a mean average precision of 88.5 % was achieved for detection, and a higher-order tracking accuracy of 88.7 % was achieved for tracking, indicating the suitability of deep learning methods for predicting the state of rotifers. In addition, this research will contribute to the development of the field by releasing the trained model and code for visualizing the tracking results, as well as an annotated dataset with over 30,000 instances.

Abstract This study compared the multiple object tracking (MOT) performance of athletes vs. non-athletes and expert athletes vs. novice athletes by systematically reviewing and meta-analyzing the literature. A systematic literature search was conducted using five databases for articles published until July 2024. Healthy people were included, specifically classified as athletes and non-athletes, or experts and novices. Potential sources of heterogeneity were selected using a random-effects model. Moderator analyses were also performed. A total of 23 studies were included in this review. Regarding the overall effect, athletes were significantly better at MOT tasks than non-athletes, and experts performed better than novices. Subgroup analyses showed that expert athletes had a significantly larger effect than novices, and that the type of sport significantly moderated the difference in MOT performance between the two groups. Meta-regression revealed that the number of targets and duration of tracking moderated the differences in performance between experts and novices, but did not affect the differences between athletes and non-athletes. This meta-analysis provides evidence of performance advantages for athletes compared with nonathletes, and experts compared with novices in MOT tasks. Moreover, the two effects were moderated by different factors; therefore, future studies should classify participants more specifically according to sports levels.

This article systematically evaluates and models how brightness affects performance in Multiple Object Tracking (MOT) within screen-based environments. MOT proficiency is essential across various fields, and comprehending the elements that impact MOT performance holds significance for occupational health and safety. While previous studies have scrutinized the influence of brightness on object recognition, its repercussions on MOT performance in screen-based environments remain comparatively less comprehended. This research aims to bridge this gap by delving into the distinct and combined impacts of brightness-related factors on MOT performance. Additionally, it seeks to construct a computational model that can forecast MOT performance across diverse brightness conditions. The outcomes of this study will offer valuable insights into core psychological processes, thereby steering the development of more efficient visual displays to enhance occupational health and safety.Our findings revealed a significant correlation between brightness levels and MOT performance, with optimal tracking observed at medium brightness levels. Additionally, complex object motion patterns were found to exacerbate the challenges of tracking in low brightness settings. These insights have direct implications for screen-based interfaces, suggesting the need for adaptive brightness settings based on the content's complexity and the user's task.

Abstract Background The present study aims to investigate the potential impact of eight sessions of Multiple Object Tracking (MOT) training on the executive function in basketball players. The purpose of the study was primarily to observe the effects of MOT training with and without feedback on the executive function of basketball players. Methods A sample of fifty-eight participants was selected from college students enrolled in a university basketball special selection class. The participants were divided into three equal groups. The first group received MOT training with instant feedback and was called feedback group, the second group received MOT training without instant feedback and was called no feedback group, and the third group did not receive any intervention and was called control group. Results After eight sessions of MOT training, feedback group demonstrated the best performance in the Go/No-go task and the 3-back task. After eight sessions of MOT training, there was no significant difference in test scores on the Stroop task between the feedback and no feedback groups. There was also no significant difference in test scores between the feedback and no feedback groups on the 2-back task after eight sessions of MOT training. The findings of this study suggest that MOT training can effectively enhance the executive function of basketball players. Conclusions MOT training was found to enhance the executive function of basketball players, irrespective of whether they received instant feedback. However, the feedback group exhibited superior improvements in the Go/No-go task and the 3-back task.

This study compared the multiple object tracking (MOT) performance of athletes vs. non-athletes and expert athletes vs. novice athletes by systematically reviewing and meta-analyzing the literature. A systematic literature search was conducted using five databases for articles published until July 2024. Healthy people were included, specifically classified as athletes and non-athletes, or experts and novices. Potential sources of heterogeneity were selected using a random-effects model. Moderator analyses were also performed. A total of 23 studies were included in this review. Regarding the overall effect, athletes were significantly better at MOT tasks than non-athletes, and experts performed better than novices. Subgroup analyses showed that expert athletes had a significantly larger effect than novices, and that the type of sport significantly moderated the difference in MOT performance between the two groups. Meta-regression revealed that the number of targets and duration of tracking moderated the differences in performance between experts and novices, but did not affect the differences between athletes and non-athletes. This meta-analysis provides evidence of performance advantages for athletes compared with nonathletes, and experts compared with novices in MOT tasks. Moreover, the two effects were moderated by different factors; therefore, future studies should classify participants more specifically according to sports levels. [ABSTRACT FROM AUTHOR]

Analyzing traffic flow based on data from traffic monitoring is an essential component of intelligent transportation systems. In most traffic scenarios, vehicles are the primary targets, so multi-object tracking of vehicles in traffic monitoring is a critical subject. In view of the current difficulties, such as complex road conditions, numerous obstructions, and similar vehicle appearances, we propose a detection-based multi-object vehicle tracking algorithm that combines motion and appearance cues. Firstly, to improve the motion prediction accuracy, we propose a Kalman filter that adaptively updates the noise according to the motion matching cost and detection confidence score, combined with exponential transformation and residuals. Then, we propose a combined distance to utilize motion and appearance cues. Finally, we present a trajectory recovery strategy to handle unmatched trajectories and detections. Experimental results on the UA-DETRAC dataset demonstrate that this method achieves excellent tracking performance for vehicle tracking tasks in traffic monitoring perspectives, meeting the practical application demands of complex traffic scenarios. [ABSTRACT FROM AUTHOR]

Background: The present study aims to investigate the potential impact of eight sessions of Multiple Object Tracking (MOT) training on the executive function in basketball players. The purpose of the study was primarily to observe the effects of MOT training with and without feedback on the executive function of basketball players. Methods: A sample of fifty-eight participants was selected from college students enrolled in a university basketball special selection class. The participants were divided into three equal groups. The first group received MOT training with instant feedback and was called feedback group, the second group received MOT training without instant feedback and was called no feedback group, and the third group did not receive any intervention and was called control group. Results: After eight sessions of MOT training, feedback group demonstrated the best performance in the Go/No-go task and the 3-back task. After eight sessions of MOT training, there was no significant difference in test scores on the Stroop task between the feedback and no feedback groups. There was also no significant difference in test scores between the feedback and no feedback groups on the 2-back task after eight sessions of MOT training. The findings of this study suggest that MOT training can effectively enhance the executive function of basketball players. Conclusions: MOT training was found to enhance the executive function of basketball players, irrespective of whether they received instant feedback. However, the feedback group exhibited superior improvements in the Go/No-go task and the 3-back task. [ABSTRACT FROM AUTHOR]

Recent advancements in simultaneous localization and mapping (SLAM) have significantly improved the handling of dynamic objects. Traditionally, SLAM systems mitigate the impact of dynamic objects by extracting, matching, and tracking features. However, in real-world scenarios, dynamic object information critically influences decision-making processes in autonomous navigation. To address this, we present a novel approach for incorporating dynamic object information into map representations, providing valuable insights for understanding movement context and estimating collision risks. Our method leverages on-site mobile robots and multiple object tracking (MOT) to gather activation levels. We propose a multimodal map framework that integrates occupancy maps obtained through SLAM with Gaussian process (GP) modeling to quantify the activation levels of dynamic objects. The Gaussian process method utilizes a map-based grid cell algorithm that distinguishes regions with varying activation levels while providing confidence measures. To validate the practical effectiveness of our approach, we also propose a method to calculate additional costs from the generated maps for global path planning. This results in path generation through less congested areas, enabling more informative navigation compared to traditional methods. Our approach is validated using a diverse dataset collected from crowded environments such as a library and public square and is demonstrated to be intuitive and to accurately provide activation levels. [ABSTRACT FROM AUTHOR]

Visual motion estimation is a well-studied challenge in autonomous navigation. Recent work has focused on addressing multimotion estimation in highly dynamic environments. These environments not only comprise multiple, complex motions but also tend to exhibit significant occlusion. Estimating third-party motions simultaneously with the sensor egomotion is difficult because an object's observed motion consists of both its true motion and the sensor motion. Most previous works in multimotion estimation simplify this problem by relying on appearance-based object detection or application-specific motion constraints. These approaches are effective in specific applications and environments but do not generalize well to the full multimotion estimation problem (MEP). This paper presents Multimotion Visual Odometry (MVO), a multimotion estimation pipeline that estimates the full SE (3) trajectory of every motion in the scene, including the sensor egomotion, without relying on appearance-based information. MVO extends the traditional visual odometry (VO) pipeline with multimotion segmentation and tracking techniques. It uses physically founded motion priors to extrapolate motions through temporary occlusions and identify the reappearance of motions through motion closure. Evaluations on real-world data from the Oxford Multimotion Dataset (OMD) and the KITTI Vision Benchmark Suite demonstrate that MVO achieves good estimation accuracy compared to similar approaches and is applicable to a variety of multimotion estimation challenges. [ABSTRACT FROM AUTHOR]

Recently, to address the multiple object tracking (MOT) problem, we harnessed the power of deep learning-based methods. The tracking-by-detection approach to multiple object tracking (MOT) involves two primary steps: object detection and data association. In the first step, objects of interest are detected in each frame of a video. The second step establishes the correspondence between these detected objects across different frames to track their trajectories. This paper proposes an efficient and unified data association method that utilizes a deep feature association network (deepFAN) to learn the associations. Additionally, the Structural Similarity Index Metric (SSIM) is employed to address uncertainties in the data association, complementing the deep feature association network. These combined association computations effectively link the current detections with the previous tracks, enhancing the overall tracking performance. To evaluate the efficiency of the proposed MOT framework, we conducted a comprehensive analysis of the popular MOT datasets, such as the MOT challenge and UA-DETRAC. The results showed that our technique performed substantially better than the current state-of-the-art methods in terms of standard MOT metrics. [ABSTRACT FROM AUTHOR]

Agility, defined as the ability to rapidly respond to unforeseen events, constitutes a central performance component in football. Existing agility training approaches often focus on change of direction that does not reflect the complex motor-cognitive demands on the pitch. The objective of this study is to examine the effects of a novel motor-cognitive dual-task agility training (Multiple-object tracking integrated into agility training) on agility and football-specific test performance parameters, compared to agility and a change of direction (COD) training. Adult male amateur football players (n = 42; age: 27±6; height: 181±7cm; weight: 80±12kg) were randomly allocated to one of the three intervention groups (COD, agility, agility + multiple object tracking). The Loughborough Soccer Passing Test (LSPT), a dribbling test with/without cognitive task as well as the Random Star Run (with/without ball) and the modified T-Test were assessed before and after a 6-week training period. Time effects within the T-Test (F = 83.9; p < 0.001; η² = 0.68) and dribbling test without cognitive task (F = 23.9; p < 0.001; η² = 0.38) with improvements of all intervention groups (p < 0.05) were found. Dribbling with cognitive task revealed a time effect (F = 7.8; p = 0.008; η² = 0.17), with improvements exclusively in the agility and dual-task agility groups (p < 0.05). Random Star Run with and without ball exhibited a time (F = 38.8; p < 0.001; η² = 0.5; F = 82.7; p < 0.001; η² = 0.68) and interaction effect (F = 14.14; p < 0.001; η² = 0.42; F = 27.8; p < 0.001; η² = 0.59), with improvements for the agility and dual-task agility groups. LSPT showed no time, group or interaction effect. The effects of change of direction training are limited to change of direction and dribbling test performance within preplanned scenarios. In contrast, motor-cognitive agility interventions result in notable enhancements in football-specific and agility tests, incorporating decision-making and multitasking components. No differences were observed between agility and agility + multiple object tracking. To achieve a transfer to game-relevant performance, coaches should focus on integrating cognitive challenges into motor training. [ABSTRACT FROM AUTHOR]

In dynamic environments, a central task of the attentional system is to keep track of objects changing their spatial location over time. In some instances, it is sufficient to track only the spatial locations of moving objects (i.e., multiple object tracking; MOT). In other instances, however, it is also important to maintain distinct identities of moving objects (i.e., multiple identity tracking; MIT). Despite previous research, it is not clear whether MOT and MIT performance emerge from the same tracking mechanism. In the present report, we study gaze coherence (i.e., the extent to which participants repeat their gaze behaviour when tracking the same object locations twice) across repeated MOT and MIT trials. We observed more substantial gaze coherence in repeated MOT trials compared to the repeated MIT trials or mixed MOT-MIT trial pairs. A subsequent simulation study suggests that MOT is based more on a grouping mechanism than MIT, whereas MIT is based more on a target-jumping mechanism than MOT. It thus appears unlikely that MOT and MIT emerge from the same basic tracking mechanism. [ABSTRACT FROM AUTHOR]

Multiple object tracking (MOT) and multiple identity tracking (MIT) each measure the ability to track moving objects visually. While prior investigators have mainly compared athletes and non-athletes on MOT, MIT more closely resembles dynamic real-life environments. Here we compared the performance and gaze behavior of handball players with non-athletes on both MOT and MIT. Since previous researchers have shown that MOT and MIT engage different eye movement strategies, we had participants track 3–5 targets among 10 moving objects. In MOT, the objects were identical, while in MIT they differed in shape and color. Although we observed no group differences for tracking accuracy, the eye movements of athletes were more target-oriented than those of non-athletes. We concluded that tasks and stimuli intended by researchers to demonstrate that athletes' show better object tracking than non-athletes should be specific to the athletes' type of sport and should use more perception-action coupled measures. An implication of this conclusion is that the differences in object tracking skills between athletes and non-athletes is highly specific to the skills demanded by the athletes' sport. [ABSTRACT FROM AUTHOR]

Accurate yield estimation of passion fruits is essential for planning acreage and harvest timing. However, due to the complexity of the natural environment and tracking instability, the existing yield estimation methods suffer from excessively large models that are difficult to deploy or repetitive counting of fruit. Therefore, an improved approach for efficient passion fruit yield estimation was proposed using the lightweight YOLOv5s and improved DeepSORT. First, the video is fed into the proposed lightweight YOLOv5s called YOLOv5s-little to obtain coordinates and confidence information about the fruits within each frame. Then, the information obtained from the detection model is input into improved DeepSORT for continuous frame tracking of passion fruit. Considering the frequent error IDs (ID switching), two improvements based on DeepSORT are proposed: delaying the creation of tracks and adding a second round of IoU matching. Finally, to overcome the problem of repetitive counting, a specific tracking counting method based on the track information and state is used for accurate passion fruit counting. Our method achieved a competitive result in tests. YOLOv5s-little detector achieved precision of 98.9%, 98.3% recall, 99.5% mAP, and only 0.9MB model size. The improved DeepSORT algorithm achieved higher order tracking accuracy (HOTA) of 79.6%, multi-object tracking accuracy (MOTA) of 92.58%, identification F1 (IDF1) of 95.02%, and ID switch (IDSW) of 11 respectively. Compared with DeepSORT, it improved by 4.66%, 1.8%, and 9.16% in HOTA, MOTA and IDF1, respectively, and IDSW improved the most with 85%. Compared with FairMOT and TransTrack, the HOTA of YOLOv5s-little + improved DeepSORT achieved improvements of 11.56% and 25.24%, respectively. The statistical average counting accuracy of our proposed counting method reaches 95.1%, which is a 7.09% improvement over the maximum ID value counting method. The counting results from test videos are highly correlated with the manual counting results ( R 2 = 0.96), indicating that the counting method has high accuracy and effectiveness. These results show that YOLOv5s-little + improved DeepSORT can meet the practical needs of passion fruit yield estimation in real scenarios. [ABSTRACT FROM AUTHOR]

As a crucial task in the field of computer vision, object tracking models are widely used in various application domains, such as autonomous driving. However, existing multiple object tracking methods still face challenges in accurately and efficiently tracking moving multi-targets in real time. This paper presents BEVEFNet, a camera-LiDAR multi-target tracking model based on multistage fusion, which effectively utilizes the semantic information from optical images and the spatial and geometric information from LiDAR data to unify multi-modal features in a shared Bird's Eye View(BEV) representation space. By leveraging LiDAR data to complement optical images, multi-level fusion is achieved at both the feature and decision levels. The proposed efficient sparse 3D feature extraction network significantly enhances the speed of multiple object tracking by incorporating sparse convolution. Experiments conducted on the nuSences dataset demonstrate that BEVEFNet achieves an AMOTA of 69.7, improving the accuracy of multiple object tracking. [ABSTRACT FROM AUTHOR]

This study aimed to assess the impact of biological sex, concussion history, and type of sport on the baseline NeuroTracker performance, a test/train three-dimensional multiple object tracking paradigm used in sport contexts, in university level varsity athletes. A total of 136 university level varsity athletes participating in male ice hockey, male or female soccer, female volleyball, and mixed biological sex cheerleading underwent preseason NeuroTracker baseline assessments. Significant differences in NeuroTracker performance were observed based on biological sex (p

In this study, an improved stable multi-object simple online and real-time tracking (StableSORT) algorithm that was specifically designed for maritime environments was proposed to address challenges such as camera instability and irregular object motion. Specifically, StableSORT integrates a buffered IoU (B-IoU) and an observation-adaptive Kalman filter (OAKF) into the StrongSORT framework to improve tracking accuracy and robustness. A dataset was collected along the southern coast of Korea using a small autonomous surface vehicle to capture real-world maritime conditions. On this dataset, StableSORT achieved a 2.7% improvement in HOTA, 4.9% in AssA, and 2.6% in IDF1 compared to StrongSORT, and it significantly outperformed ByteTrack and OC-SORT by 84% and 69% in HOTA, respectively. These results underscore StableSORT’s ability to maintain identity consistency and enhance tracking performance under challenging maritime conditions. The ablation studies further validated the contributions of the B-IoU and OAKF modules in maintaining identity consistency and tracking accuracy under challenging maritime conditions.

Multiple object tracking (MOT) in unmanned surface vehicle (USV) videos has many application scenarios in the military and civilian fields. State-of-the-art MOT methods first extract a set of detections from the video frames, then utilize IoU distance to associate the detections of current frame and tracklets of last frame, and finally adopt linear Kalman filter to estimate the current position of tracklets. However, some problems in USV videos seriously affect the tracking performance, such as low frame rate, wobble of observation platform, nonlinear motion of objects, small objects and ambiguous appearance. In this paper, we fully explore the motion cue in USV videos and propose a simple but effective tracker, named MotionTrack. Equipping with YOLOv7 as object detector, the data association of MotionTrack is mainly composed of cascade matching with Gaussian distance module and observation-centric Kalman filter module. We validate the effectiveness with extensive experiments on the recent Jari-Maritime-Tracking-2022 dataset, achieving new state-of-the-art 46.9 MOTA, 49.2 IDF1 with 35.2 FPS running speed on a single 3090 GPU. The source code, pretrained models with deploy versions are released at https://github.com/lzq11/MotionTrack. [ABSTRACT FROM AUTHOR]

Multiple object tracking is challenging due to the complex spatiotemporal relationship and the occlusion of different targets. Most existing methods use separate neural networks to generate robust features for data association inside the targets' bounding boxes. Unlike existing methods that just consider each target and the trajectory formed independently while ignoring the context information between tracklet and intra-frame detection, this paper proposes a tracking algorithm that combines multi-channel features with learnable graph matching. In brief, we use global and local salient features to model the appearance of intra-frame targets based on parallel graphs and mining high-order intra-context relationships using a completely undirected graph relationship between trajectories and detections. On this basis, we establish a multi-channel message communication mechanism through the maximum pooling mechanism to transmit the maximum dissimilation registration results to the graph matching layer, thereby forming a more robust allocation result in the data association process. Lastly, the evaluations on MOTChallenge benchmarks verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

To reduce computational redundancies, a common approach is to integrate detection and re-identification (Re-ID) into a single network in multi-object tracking (MOT), referred to as "tracking by detection." Most of the previous research has focused on resolving the conflict between the detection and Re-ID branches, considering it a simple coupling. In our work, we uncover that the entangled state between the detection and Re-ID tasks is much more complex than previous idea, resulting in a form of competition that degrades performance. To address the preceding issue, we propose a feature disentanglement network that deeply disentangles the intricately interwoven latent space of features and provides differentiated feature maps for each individual task. Furthermore, considering the demand for shallow semantic features in the feature re-ID branch, we also introduce a feature re-globalization module to enrich the shallow semantics. By integrating two distinct networks into a one-shot online MOT method, we develop a robust MOT tracker (named HDGTrack). We conduct extensive experiments on a number of benchmarks, and our experimental results demonstrate that our method significantly outperforms state-of-the-art MOT methods. Besides, HDGTrack is efficient and can run at 13.9 (MOT17) and 8.7 (MOT20) frames per second. [ABSTRACT FROM AUTHOR]

The demand for smart surveillance systems has been driven by the ubiquity of cameras in modern society. Among the crucial tasks in such systems, person re-identification (re-ID) and multiple object tracking (MOT) are paramount. Despite the common photographic challenges they share, these tasks serve distinct objectives, complicating their integration into a unified system. To be specific, most existing work lacks a comprehensive study on effectively integrating re-ID models with object trackers to achieve optimal MOT performance. A decrease in MOT performance may occur without proper calibration for the integration of both components despite using an enhanced re-ID model for the tracker. To address these issues, we propose a straightforward and effective solution that integrates an improved re-ID model into a MOT framework, the BoT-SORT tracker, ensuring enhanced MOT performance on the well-known benchmarks MOT17 and MOT20 with fewer parameters for tuning. Recognizing the sub-optimal performance of existing re-ID models with their original loss functions, we introduce a novel loss function that incorporates a polynomial cross-entropy component to enhance training efficiency on closed-world datasets. As a result, the re-ID model trained with the proposed method achieves state-of-the-art performance on Market1501 and DukeMTMC datasets, and is subsequently applied to a BoT-SORT tracker with a post-processing re-ranking module for MOT. Experimental results show that the proposed method achieves 81.2% and 77.8% MOTA scores on MOT17 and MOT20 datasets, respectively, outperforming the state-of-the-art MOT methods.