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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]

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A surveillance system using object tracking enhances security by continuously monitoring and analyzing movements. However, tracking human movement in surveillance systems still presents challenges, especially in crowded environments. These challenges, such as occlusion, similar appearance, and deformation, can affect the accuracy and precision of object tracking. To address these issues, we introduced a new approach combining HSV-template matching with the MEESPSO algorithm. In this approach, HSV-template matching continuously detects the target object in sequence images, while the Midrange Exploration Exploitation Searching Particle Swarm Optimization (MEESPSO) algorithm searches for the target location in the frames. The proposed method was tested using six video datasets from Performance Evaluation of Tracking and Surveillance 2009 (PETS09) and Multiple Object Tracking Challenge 2020 (MOT20), encountering crowded environments that introduced challenges like occlusion, similar appearance, and deformation. Experiments on both PETS09 and MOT20 datasets demonstrated that the proposed method improved tracking performance by over 4.67% in accuracy and 15% in precision compared to existing studies, where the result effectively addresses the crowded environment challenges identified in this research.

In recent years, there has been a growing recognition of the importance of balance and coordination in maintaining overall health, particularly among the elderly population. As societies around the world experience demographic shifts with an increasing number of elderly individuals, age-related declines in balance and coordination can lead to an elevated risk of falls and fall-related injuries, making it a significant public health concern. The need for effective balance rehabilitation and fall prevention strategies has become paramount. This paper presents a motion platform designed for balance rehabilitation exercises using exergames. The system consists of a 3 degrees of freedom (3DOF) platform actuated by four servomotors controlled by Omron-DeltaTau real-time motion controller. As a novelty with respect to other State of Art (SoA) actuated platforms, in the present work the movements of the user's body, which determine the platform motion, are tracked by exploiting a markerless motion capture system with an RGB camera and pose-estimation algorithm MediaPipe. The human displacement is used to generate the input for the roll rotation of the platform according to a one d.o.f. transfer function, whose parameters can be set to get different levels of difficulties in the exercise. The device offers therefore the flexibility to set different parameters to modify the level of the users' training. The proposed device has the potential for a larger variety of rehabilitation activities and exergames. This paper describes the elements adopted for the implementation of this human-in-the-loop system, presents some preliminary tests to explore the influence of transfer function parameters on human and platform movements, and concludes with future development possibilities. [ABSTRACT FROM AUTHOR]

Human activity tracking using wearable technology is an important research field of study and is employed in many applications such as healthcare, aged care, fitness, and tracking activity. Although this field has been well addressed by researchers in the last decade that most of the research is done with tightly fixed sensors (either to the body or to the tight garment), while in contrast, the majority of our daily clothes are loosely fitting so we must be interested in this fields but the main challenge in tracking/recognizing activities in loose clothing is the higher level of noise generated by the movement of clothing compared fixed sensor. This paper reviews recent research on human activity tracking techniques that use sensors that are attached or embedded in loose-fitted clothes. It is predicted from recent studies that many low-cost, reliable, and socially acceptable wearable devices will be available to aid activity tracking. Survey also predicts an increase in the interest for wearables in loose-fitting clothes due to their wide real-time applications. Most of the reviewed work based heavily on off-shelf microcontroller board and sensor, which are characterized by their moderate performance. Hence most of the reviewed work used machine learning to enhance results classification. [ABSTRACT FROM AUTHOR]

The past years have witnessed the rapid conceptualization and development of wireless sensing based on Channel State Information (CSI) with commodity WiFi devices. Recent studies have demonstrated the vast potential ofWiFi sensing in detection, recognition, and estimation applications. However, the widespread deployment of WiFi sensing systems still faces a significant challenge: how to ensure the sensing performance when exposing a pre-trained sensing system to new domains, such as new environments, different configurations, and unseen users, without data collection and system retraining. This survey provides a comprehensive review of recent research efforts on cross-domain WiFi Sensing.We first introduce the mathematical model of CSI and explore the impact of different domains on CSI. Then we present a general workflow of cross-domain WiFi sensing systems, which consists of signal processing and cross-domain sensing. Five cross-domain sensing algorithms, including domain-invariant feature extraction, virtual sample generation, transfer learning, few-shot learning and big data solution, are summarized to show how they achieve high sensing accuracy when encountering new domains. The advantages and limitations of each algorithm are also summarized and the performance comparison is made based on different applications. Finally, we discuss the remaining challenges to further promote the practical usability of cross-domain WiFi sensing systems. [ABSTRACT FROM AUTHOR]

Human motion tracking is a valuable task for many medical applications. Marker-based optoelectronic systems are considered the gold standard in human motion tracking. However, their use is not always feasible in clinics and industrial environments. On the other hand, marker-less sensors became valuable tools, as they are inexpensive, noninvasive and easy to use. However, their accuracy can depend on many factors including sensor positioning, light conditions and body occlusions. In this study, following previous works on the feasibility of marker-less systems for human motion monitoring, we investigate the performance of the Microsoft Azure Kinect sensor in computing kinematic and dynamic measurements of static postures and dynamic movements. According to our knowledge, it is the first time that this sensor is compared with a Vicon marker-based system to assess the best camera positioning while observing the upper body part movements of people performing several tasks. Twenty-five healthy volunteers were monitored to evaluate the effects of the several testing conditions, including the Azure Kinect positions, the light conditions, and lower limbs occlusions, on the tracking accuracy of kinematic, dynamic, and motor control parameters. From the statistical analysis of the performed measurements, the camera in the frontal position was the most reliable, the lighting conditions had almost no effects on the tracking accuracy, while the lower limbs occlusion worsened the accuracy of the upper limbs. The assessment of human static postures and dynamic movements based on experimental data proves the feasibility of applying the Azure Kinect to the biomechanical monitoring of human motion in several fields.

Millimeter-wave (mmWave) radars attain high resolution without compromising privacy while being unaffected by environmental factors such as rain, dust, and fog. This study explores the challenges of using mmWave radars for the simultaneous detection of people and small animals, a critical concern in applications like indoor wireless energy transfer systems. This work proposes innovative methodologies for enhancing detection accuracy and overcoming the inherent difficulties posed by differences in target size and volume. In particular, we explore two distinct positioning scenarios that involve up to four mmWave radars in an indoor environment to detect and track both humans and small animals. We compare the outcomes achieved through the implementation of three distinct data-fusion methods. It was shown that using a single radar without the application of a tracking algorithm resulted in a sensitivity of 46.1%. However, this sensitivity significantly increased to 97.10% upon utilizing four radars using with the optimal fusion method and tracking. This improvement highlights the effectiveness of employing multiple radars together with data fusion techniques, significantly enhancing sensitivity and reliability in target detection.

Introduction: Human sex trafficking is widespread and a nationally recognized public health crisis. Trafficked individuals and survivors often utilize the healthcare system, yet healthcare professionals cite a lack of formal training to identify and provide appropriate care. This study aims to increase obstetrics/gynecology, emergency medicine, and family medicine residents’ knowledge and confidence when caring for individuals involved in sex trafficking. Methods: Obstetrics/gynecology, emergency medicine, and family medicine residents voluntarily attended an evidence-based education session and received an electronic pre-, immediate post-, and 5-month post-session survey measuring knowledge, attitudes, beliefs, and confidence. Validated survey items were adapted from previous studies. Paired t-tests (p

In this paper, we propose a new approach for tracking each human in a surveillance camera network with various resolution cameras. When tracking human on multiple non-overlapping cameras, the traditional appearance features are easily affected by various camera viewing conditions. To overcome this limitation, the proposed system utilizes facial information along with appearance information. In general, human images captured by the surveillance camera are often low resolution, so it is necessary to be able to extract useful features even from low-resolution faces to facilitate tracking. In the proposed tracking scheme, texture-based face descriptor is exploited to extract features from detected face after face frontalization. In addition, when the size of the face captured by the surveillance camera is very small, a super-resolution technique that enlarges the face is also exploited. The experimental results on the public benchmark Dana36 dataset show promising performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The number of older people needing healthcare is a growing global phenomenon. The assistance in long-term care comprises a complex of medical, nursing, rehabilitation, and social assistance services. The cost is substantial, but technology can help reduce spending by ensuring efficient health services and improving the quality of life. Advances in artificial intelligence, wireless communication systems, and nanotechnology allow the creation of intelligent home care systems avoiding hospitalization with evident cost containment. They are capable of ensuring functions of recognition of activities, monitoring of vital functions, and tracking. However, it is essential to also have information on location in order to be able to promptly intervene in case of unforeseen events or assist people in carrying out activities in order to avoid incorrect behavior. In addition, the automatic detection of physical activities performed by human subjects is identified as human activity recognition (HAR). This work presents an overview of the positioning system as part of an integrated HAR system. Lastly, this study contains each technology's concepts, features, accuracy, advantages, and limitations. With this work, we want to highlight the relationship between HAR and the indoor positioning system (IPS), which is poorly documented in the literature. [ABSTRACT FROM AUTHOR]

This paper presents a Bayesian technique for navigating a mobile robot to safely escort a human to their destination. A mathematical analysis of escorting identifies the significance of human intention inference for escorting success. The proposed Bayesian technique observes the head pose of the human walking behind the robot to infer their intention. The future human pose is then predicted by fusing two independent predictions using Gaussian fusion. One prediction method uses the observed intention, and the other prediction uses a physics‐based model. Both prediction methods initially use particle filters to best model nonlinear and non‐Gaussian motion. The robot goal pose is determined from the predicted human motion and adjusted if necessary leveraging the unoccupied distance map to avoid obstacles and successfully and safely enable autonomous escorting. Experimental analysis shows that the incorporation of the proposed human intention model reduces human position prediction error by approximately 33% when turning, improving escorting accuracy by 50%. The incorporation of the proposed obstacle avoidance has further demonstrated its need and efficacy for successful and safe autonomous escorting. [ABSTRACT FROM AUTHOR]

The underground mine environment is dangerous and harsh, tracking and detecting humans based on computer vision is of great significance for mine safety monitoring, which will also greatly facilitate identification of humans using the symmetrical image features of human organs. However, existing methods have difficulty solving the problems of accurate identification of humans and background, unstable human appearance characteristics, and humans occluded or lost. For these reasons, an improved aberrance repressed correlation filter (IARCF) tracker for human tracking in underground mines based on infrared videos is proposed. Firstly, the preprocess operations of edge sharpening, contrast adjustment, and denoising are used to enhance the image features of original videos. Secondly, the response map characteristics of peak shape and peak to side lobe ratio (PSLR) are analyzed to identify abnormal human locations in each frame, and the method of calculating the image similarity by generating virtual tracking boxes is used to accurately relocate the human. Finally, using the value of PSLR and the highest peak point of the response map, the appearance model is adaptively updated to further improve the robustness of the tracker. Experimental results show that the average precision and success rate of the IARCF tracker in the five underground scenarios reach 0.8985 and 0.7183, respectively, and the improvement of human tracking in difficult scenes is excellent. The IARCF tracker can effectively track underground human targets, especially occluded humans in complex scenes. [ABSTRACT FROM AUTHOR]

We address the problem of pedestrian tracking in videos of crowded scene which are captured by first-person viewpoint. The constant motion of camera and pedestrian makes this task challenging. The prime challenges are natural head motion of wearer and target loss and reappearance in a later frame, due to frequent changes in field of view. We propose that the use of first-person vision specific optical flow information and also the modification in the update process along with search region of trackers are useful to identify a lost target in a later frame. This process is termed re-identification in this paper. The specific trackers modified are MEEM and STRUCK. In addition to re-identification we achieve scale invariant tracking (upto 50 % scale variation) and speed up by a factor of 2. We name our tracker as EgoTracker, since it utilizes the information which is specific to egocentric vision. [ABSTRACT FROM AUTHOR]

This research proposes a method for developing an automated person tracking system. At present, many object inspection systems use deep learning and deep learning methods are widely used in object detection methods. Object tracking in a video image is another example. In this research, a new approach is developed to track and identify the person by using deep learning. This method consists of two steps. The first step, deep learning is used to examine the person featured in digital images. The center point of the detected person is used for the next step. In the last step, two-stepping motors are forced to move on the vertical and horizontal axes. The moving distance of the two axes is calculated by using the previous step center point. This movement of stepping motors will control the laser pointer for pointing out the person appearing in the digital image. The experiment and result of this method demonstrate that the method can point to the person within an image. [ABSTRACT FROM AUTHOR]

In previous years, the world has gone through several natural disasters like Tsunami, earthquakes, floods, tornadoes, hurricanes, cyclones, etc., and manufactured disasters such as stampedes, fire, terror attacks, etc. A large number of causalities are reported, with a massive loss to life, economy, and other things. Knowing this, we should make a transit from a reliable and flexible disaster management approach to a proactive one by leveraging advances in science and technology. A colossal increase in worldwide population points out that the occurrence of a crowd at any place is becoming more and more familiar with each passing day. It is undeniable that these mass gatherings often become a source of a crowd-related catastrophe such as sudden escape, terror attacks, mob lynching, human stampede, or human crushing. Prior research on the crowd's social, psychological, and computational dynamics has indicated that the crowd's behavior under such devastating conditions is greatly decisive for crowd safety, its access or escape from the affected region, and emergency evacuation. Despite this, there is a certain paucity of pragmatic research on extreme crowd-related use cases and how to deal with such situations effectively. Through the past years, people and media have shared the details of such happenings and their experiences on a micro-level through various social network mediums. Attempts are being made to analyze this data using advanced technological tools and methods to extract the trends out of such happenings and predict any future happenings so that countermeasures can be taken and they can be prevented. This paper makes a structured literature assessment on the current scenario and systematically surveys the studies made in this field. It paves the path for future rendezvous in this area to unearth the hidden gold mine of information along the timeline. Also, an attempt is made to develop a technological solution or system that may help achieve an elevated level of social security via holistic video surveillance capable of detecting any crowd-related anomaly and proactively warning the concerned authorities about any such casualty. This will ensure that crowd disasters can be prevented well in time by gaining prior insights about them. The system is developed that encompasses everything from human detection, tracking, and counting to any abnormal behavior detection. The same has been achieved with 93.33% accuracy. [ABSTRACT FROM AUTHOR]

In recent decades, the growing deployment of Closed-Circuit Television (CCTV) systems for crime prevention and facility security has accelerated the importance of intelligent surveillance technologies. One of the primary challenges in this field includes varying viewpoints and adverse weather conditions that significantly compromise the accuracy of human tracking and anomaly detection. Moreover, conventional surveillance systems often focus only on specific events within limited scenarios, which restricts their applicability. Existing deep learning approaches also face limitations in adaptability to environmental variations, mainly due to the high maintenance costs involved in data collection. To address these challenges, we present a comprehensive surveillance system that utilizes deep learning to enhance human tracking and anomaly detection across diverse environments. Our approach includes the implementation of novel object filtering algorithms that decrease false positive rates and improve tracking precision. Additionally, our system is capable of monitoring multiple types of abnormal events, such as intrusion, loitering, abandonment, and arson. We further introduce a prompt-based recognition mechanism that enables active user participation in identifying abnormal scenes. Extensive evaluations using the Korea Internet & Security Agency CCTV datasets have demonstrated significant performance enhancements by our system, particularly under challenging weather conditions. Moreover, our system achieved competitive accuracy on the ABODA and FireNet datasets, even without additional training. This research establishes a new baseline for practical surveillance solutions that focus on comprehensive monitoring across various abnormal scenarios. • Developed a human detection and tracking module for diverse environmental conditions. • Proposed object filter reduces false positives in human tracking. • Implemented proactive system alerts on intrusion, loitering, abandonment, and arson. • System evaluation extensively conducted under RTSP in real-world CCTV conditions. • Prompt-based approach enhances system flexibility and reduces operational costs. [ABSTRACT FROM AUTHOR]

In recent years, the unmanned economy has begun to develop rapidly worldwide. Unmanned shops use technology to replace human personnel and increase efficiency. This technology has increased the demand for accurate and efficient camera surveillance systems. Camera handoff is a crucial step in the tasks of continuous target tracking and the maintenance of consistent cross-camera target marking in a multicamera surveillance system. In this study, we proposed a method of indoor multicamera handoff. To ensure continuous target tracking, the minimum number of required frames was maintained and at least one camera tracked each target. We employed the background subtraction approach to detect the target. Next, we used three trackability measures to evaluate the tracking object and trigger camera handoff accordingly, selecting the optimal camera for target tracking. The three measures considered were the resolution, occlusion, and distance to the edge of the camera's field of view. When one of these reached a preset threshold with an increasing trend, the system triggered the camera handoff. [ABSTRACT FROM AUTHOR]

Industrial growth has increased the number of jobs hence increase the number of employees. Therefore, it is impossible to track the location of all employees in the same building at the same time as they are placed in a different department. In this work, a real-time indoor human tracking system is developed to determine the location of employees in a real-time implementation. In this work, the long-range (LoRa) technology is used as the communication medium to establish the communication between the tracker and the gateway in the developed system due to its low power with high coverage range besides requires low cost for deployment. The received signal strength indicator (RSSI) based positioning method is used to measure the power level at the receiver which is the gateway to determine the location of the employees. Different scenarios have been considered to evaluate the performance of the developed system in terms of precision and reliability. This includes the size of the area, the number of obstacles in the considered area, and the height of the tracker and the gateway. A real-time testbed implementation has been conducted to evaluate the performance of the developed system and the results show that the system has high precision and are reliable for all considered scenarios. [ABSTRACT FROM AUTHOR]

People's health and the world economy around worldwide are under the impact of the virus. Controlling the propagation of coronavirus is extraordinarily challenging after the seventeen months since the pandemic outbreak. This research will represent the efficiency of robots in helping humanity in facing the Corona pandemic and the global health crisis. According to the quick and widespread Covid-19 contagion globally, healthcare service demand increased staff with less availability. In this crisis, robotics is the safe solution at a low price to support people to fight Covid-19. Furthermore, it is essential to relax lockdown restrictions after the increase in the number of people vaccinated. In this context, we designed a mobile robot with a thermal temperature scanner for pedestrians and identified those with abnormal temperatures over 39°C in public places and track them in real-time. This robot features a long-lasting, lightweight battery and a high-quality thermal camera. Our primary target is to reduce the risk of transmission between people. We use a single-board computer and a thermal camera to detect unusual fevers near real-time to achieve this design and prototyping. In addition, we use image processing to detect target pedestrians and a control system to guide the robot. This robot can bypass any potential obstacles that can prevent it from moving around and cause problems. The development of this robot mainly focuses on the real-time measurement of body temperature at two meters. The compensation algorithm can coordinate between the mechanical parts of the robot and high-efficiency design. [ABSTRACT FROM AUTHOR]

The number of older people needing healthcare is a growing global phenomenon. The assistance in long-term care comprises a complex of medical, nursing, rehabilitation, and social assistance services. The cost is substantial, but technology can help reduce spending by ensuring efficient health services and improving the quality of life. Advances in artificial intelligence, wireless communication systems, and nanotechnology allow the creation of intelligent home care systems avoiding hospitalization with evident cost containment. They are capable of ensuring functions of recognition of activities, monitoring of vital functions, and tracking. However, it is essential to also have information on location in order to be able to promptly intervene in case of unforeseen events or assist people in carrying out activities in order to avoid incorrect behavior. In addition, the automatic detection of physical activities performed by human subjects is identified as human activity recognition (HAR). This work presents an overview of the positioning system as part of an integrated HAR system. Lastly, this study contains each technology’s concepts, features, accuracy, advantages, and limitations. With this work, we want to highlight the relationship between HAR and the indoor positioning system (IPS), which is poorly documented in the literature.

The underground mine environment is dangerous and harsh, tracking and detecting humans based on computer vision is of great significance for mine safety monitoring, which will also greatly facilitate identification of humans using the symmetrical image features of human organs. However, existing methods have difficulty solving the problems of accurate identification of humans and background, unstable human appearance characteristics, and humans occluded or lost. For these reasons, an improved aberrance repressed correlation filter (IARCF) tracker for human tracking in underground mines based on infrared videos is proposed. Firstly, the preprocess operations of edge sharpening, contrast adjustment, and denoising are used to enhance the image features of original videos. Secondly, the response map characteristics of peak shape and peak to side lobe ratio (PSLR) are analyzed to identify abnormal human locations in each frame, and the method of calculating the image similarity by generating virtual tracking boxes is used to accurately relocate the human. Finally, using the value of PSLR and the highest peak point of the response map, the appearance model is adaptively updated to further improve the robustness of the tracker. Experimental results show that the average precision and success rate of the IARCF tracker in the five underground scenarios reach 0.8985 and 0.7183, respectively, and the improvement of human tracking in difficult scenes is excellent. The IARCF tracker can effectively track underground human targets, especially occluded humans in complex scenes.

In this work, we investigate the use of backscattered mm-wave radio signals for the joint tracking and recognition of identities of humans as they move within indoor environments. We build a system that effectively works with multiple persons concurrently sharing and freely moving within the same indoor space. This leads to a complicated setting, which requires one to deal with the randomness and complexity of the resulting (composite) backscattered signal. The proposed system combines several processing steps: at first, the signal is filtered to remove artifacts, reflections, and random noise that do not originate from humans. Hence, a density-based classification algorithm is executed to separate the Doppler signatures of different users. The final blocks are trajectory tracking and user identification, respectively, based on Kalman filters and deep neural networks. Our results demonstrate that the integration of the last-mentioned processing stages is critical toward achieving robustness and accuracy in multiuser settings. Our technique is tested both on a single-target public data set, for which it outperforms state-of-the-art methods, and on our own measurements, obtained with a 77 GHz radar on multiple subjects simultaneously moving in two different indoor environments. The system works in an online fashion, permitting the continuous identification of multiple subjects with accuracies up to 98%, e.g., with four subjects sharing the same physical space, and with a small accuracy reduction when tested with unseen data from a challenging real-life scenario that was not part of the model learning phase. [ABSTRACT FROM AUTHOR]

Automating fall risk assessment, in an efficient, non-invasive manner, specifically in the elderly population, serves as an efficient means for implementing wide screening of individuals for fall risk and determining their need for participation in fall prevention programs. We present an automated and efficient system for fall risk assessment based on a multi-depth camera human motion tracking system, which captures patients performing the well-known and validated Berg Balance Scale (BBS). Trained machine learning classifiers predict the patient’s 14 scores of the BBS by extracting spatio-temporal features from the captured human motion records. Additionally, we used machine learning tools to develop fall risk predictors that enable reducing the number of BBS tasks required to assess fall risk, from 14 to 4–6 tasks, without compromising the quality and accuracy of the BBS assessment. The reduced battery, termed Efficient-BBS (E-BBS), can be performed by physiotherapists in a traditional setting or deployed using our automated system, allowing an efficient and effective BBS evaluation. We report on a pilot study, run in a major hospital, including accuracy and statistical evaluations. We show the accuracy and confidence levels of the E-BBS, as well as the average number of BBS tasks required to reach the accuracy thresholds. The trained E-BBS system was shown to reduce the number of tasks in the BBS test by approximately 50% while maintaining 97% accuracy. The presented approach enables a wide screening of individuals for fall risk in a manner that does not require significant time or resources from the medical community. Furthermore, the technology and machine learning algorithms can be implemented on other batteries of medical tests and evaluations.