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Automatic multiple human tracking using an adaptive hybrid GMM based detection in a crowd

MULTIMEDIA TOOLS AND APPLICATIONS 2020年第39-40期79卷 28993-29019页

作者： Karpagavalli, P. Ramprasad, A., V KLN Coll Engn Elect & Commun Engn Dept Pottapalayam Tamil Nadu India KLN Coll Engn Pottapalayam Tamil Nadu India

For a visual surveillance in a crowd, multiple human tracking is essential and of course a challenging task. Real-world applications require multiple cameras to capture crowd scenes so that a keen tracking is observed. Automatic tracking in a crowded environment is very important criteria for the surveillance. Accurate and real-time tracking in a crowd, the number of people present in the public places and shopping mall are some of the vital information for monitoring traffic violations. To provide human safety and security, surveillance like theft prevention and automated checkout provides the necessary consumer information to the managers. The conventional tracking algorithm does not handle the complex background, multi-view points, various illumination changes and severe occlusion occurring in a crowd. The above problem can be effectively handled by using the proposed Adaptive Hybrid Multiple Human tracking (AHMHT) method. The proposed work utilizes the Adaptive Hybrid Gaussian Mixture Model (AHGMM) (Karpagavalli and Ramprasad, International Journal of Multimedia Tools and Application 76(12):14129-14149,12) detected output, so that, the proposed algorithm tracks all the blobs in each frame on the basis of motion information along with the width and height information of exact blob. The experimental results demonstrate that the proposed method performs well compared to other methods. The multiple human tracking rates are improved with maximum of 91%using the proposed frame work compared with other methods. The proposed method is efficient in terms of computational time (CT) using an adaptive hybrid tracking.

关键词： tracking by detection Multiple human tracking Computer vision Automation Multiple blob detection Adaptive hybrid GMM based detection Adaptive hybrid multiple human tracking

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Enhanced detection Reliability for Human tracking Based Video Analytics 22

Enhanced Detection Reliability for Human Tracking Based Vide...

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22nd International Conference on Information Fusion (FUSION)

作者： Fu, Zeyu Lai, Xin Naqvi, Syed Mohsen Newcastle Univ Sch Engn ISC Res Grp Newcastle Upon Tyne Tyne & Wear England Southwest Petr Univ Sch Mech & Elect Engn Educ Minist Key Lab Oil & Gas Equipment Chengdu Sichuan Peoples R China

ISBN: (纸本)9780996452786

Human tracking based video analytics has become a popular approach to develop smart surveillance systems, with particular interest in anomaly detection and healthcare systems. Handling inaccurate detections plays a key element in the tracking pipeline. To achieve this, modern human tracking methods have often relied on setting up a threshold on confidence scores, but missing in-depth analysis between these scores and true human detections. This may render an undesirable selection performance. For this purpose, we firstly analyze the misalignment between the given confidence scores and true human detections in the MOT16 Challenge benchmark ill. Then we propose a global-to-local enhanced confidence rescoring strategy by exploiting the classification power of a mask region convolutional neural network (Mask R-CNN) [2], in order to mitigate the misalignment issue. Moreover, we devise an improved pruning algorithm namely Soft-aggregated non-maximal suppression (Soft-ANMS) to further enhance the detection reliability. The proposed method can be applied as a generic early processing step to any online tracking methods for robust measurement selection. Experimental results on both tasks of measurement selection and human tracking confirm the effectiveness of the proposed method.

关键词： Video analytics human tracking tracking by detection GM-PHD filter detection reliability

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PDTrack: Progressive Distance Association for Multiple Object tracking

PDTrack: Progressive Distance Association for Multiple Objec...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Yeqiang Li, Weiran Ding, Yanhao Li, Zhenbo College of Information and Electrical Engineering China Agricultural University Beijing100083 China National Innovation Center for Digital Fishery Ministry of Agriculture and Rural Affairs Beijing100083 China Key Laboratory of Smart Farming Technologies for Aquatic Animal and Livestock Ministry of Agriculture and Rural Affairs Beijing100083 China

ISBN: (纸本)9798350368741

Multiple Object tracking (MOT) has made significant progress in recent years. However, it still faces challenges such as frequent ID switches, trajectory fragmentation, and tracking losses in high-density pedestrian scenarios. To address these issues, we optimized the association algorithm based on the tracking by detection paradigm and proposed a progressive distance association matching algorithm (PDTrack). By using trajectory boxes gradually and orderly for association, we reduce computational costs. Additionally, we introduced the Disambiguation Model and Reassociation Model to reduce erroneous tracking results and avoid missed tracking objects, ensuring the integrity of the trajectories. Experimental results demonstrate that our proposed method achieves higher accuracy and fewer identity switches compared to other mainstream tracking algorithms, effectively enhancing multiple object tracking performance. © 2025 IEEE.

关键词： Computer Vision Data Association Multiple Object tracking tracking by detection

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Vision-Based Method Integrating Deep Learning detection for tracking Multiple Construction Machines

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JOURNAL OF COMPUTING IN CIVIL ENGINEERING 2021年第2期35卷

作者： Xiao, Bo Kang, Shih-Chung Univ Alberta Dept Civil & Environm Engn Edmonton AB T6G 2R3 Canada

tracking construction machines in videos is a fundamental step in the automated surveillance of construction productivity, safety, and project progress. However, existing vision-based tracking methods are not able to achieve high tracking precision, robustness, and practical processing speed simultaneously. Occlusions and illumination variations on construction sites also prevent vision-based tracking methods from obtaining optimal tracking performance. To address these challenges, this research proposes a vision-based method, called construction machine tracker (CMT), to track multiple construction machines in videos. CMT consists of three main modules: detection, association, and assignment. The detection module detects construction machines using the deep learning algorithm YOLOv3 in each frame. Then the association module relates the detection results of two consecutive frames, and the assignment module produces the tracking results. In testing, CMT achieved 93.2% in multiple object tracking accuracy (MOTA) and 86.5% in multiple object tracking precision (MOTP) with a processing speed of 20.8 frames per second when tested on four construction videos. The proposed CMT was integrated into a framework of analyzing excavator productivity in earthmoving cycles and achieved 96.9% accuracy.

关键词： Multiple object tracking Construction machines Deep learning tracking by detection

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A Lightweight Man-Overboard detection and tracking Model Using Aerial Images for Maritime Search and Rescue

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REMOTE SENSING 2024年第1期16卷 165页

作者： Zhang, Yijian Tao, Qianyi Yin, Yong Dalian Maritime Univ Nav Coll Dalian 116026 Peoples R China

Unmanned rescue systems have become an efficient means of executing maritime search and rescue operations, ensuring the safety of rescue personnel. Unmanned aerial vehicles (UAVs), due to their agility and portability, are well-suited for these missions. In this context, we introduce a lightweight detection model, YOLOv7-FSB, and its integration with ByteTrack for real-time detection and tracking of individuals in maritime distress situations. YOLOv7-FSB is our lightweight detection model, designed to optimize the use of computational resources on UAVs. It comprises several key components: FSNet serves as the backbone network, reducing redundant computations and memory access to enhance the overall efficiency. The SP-ELAN module is introduced to ensure operational speed while improving feature extraction capabilities. We have also enhanced the feature pyramid structure, making it highly effective for locating individuals in distress within aerial images captured by UAVs. By integrating this lightweight model with ByteTrack, we have created a system that improves detection accuracy from 86.9% to 89.2% while maintaining a detection speed similar to YOLOv7-tiny. Additionally, our approach achieves a MOTA of 85.5% and a tracking speed of 82.7 frames per second, meeting the demanding requirements of maritime search and rescue missions.

关键词： unmanned aerial vehicles (UAVs) object tracking object detection tracking by detection maritime search and rescue

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High-Performance detection-Based Tracker for Multiple Object tracking in UAVs

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DRONES 2023年第11期7卷

作者： Li, Xi Zhu, Ruixiang Yu, Xianguo Wang, Xiangke Changsha Univ Sci & Technol Hunan Prov Key Lab Flexible Elect Mat Genome Engn Changsha 410004 Peoples R China Natl Univ Def Technol Coll Intelligence Sci & Technol Changsha 410073 Peoples R China

As a result of increasing urbanization, traffic monitoring in cities has become a challenging task. The use of Unmanned Aerial Vehicles (UAVs) provides an attractive solution to this problem. Multi-Object tracking (MOT) for UAVs is a key technology to fulfill this task. Traditional detection-based-tracking (DBT) methods begin by employing an object detector to retrieve targets in each image and then track them based on a matching algorithm. Recently, the popular multi-task learning methods have been dominating this area, since they can detect targets and extract Re-Identification (Re-ID) features in a computationally efficient way. However, the detection task and the tracking task have conflicting requirements on image features, leading to the poor performance of the joint learning model compared to separate detection and tracking methods. The problem is more severe when it comes to UAV images due to the presence of irregular motion of a large number of small targets. In this paper, we propose using a balanced Joint detection and Re-ID learning (JDR) network to address the MOT problem in UAV vision. To better handle the non-uniform motion of objects in UAV videos, the Set-Membership Filter is applied, which describes object state as a bounded set. An appearance-matching cascade is then proposed based on the target state set. Furthermore, a Motion-Mutation module is designed to address the challenges posed by the abrupt motion of UAV. Extensive experiments on the VisDrone2019-MOT dataset certify that our proposed model, referred to as SMFMOT, outperforms the state-of-the-art models by a wide margin and achieves superior performance in the MOT tasks in UAV videos.

关键词： unmanned aerial vehicles multi-object tracking tracking by detection set-membership filter

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Joint detection and tracking in videos with identification features

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IMAGE AND VISION COMPUTING 2020年 100卷 103932-103932页

作者： Munjal, Bharti Aftab, Abdul Rafey Amin, Sikandar Brandlmaier, Meltem D. Tombari, Federico Galasso, Fabio OSRAM GmbH Parkring 33 Garching D-85748 Munich Germany Tech Univ Munich Munich Germany Sapienza Univ Rome Rome Italy

Recent works have shown that combining object detection and tracking tasks, in the case of video data, results in higher performance for both tasks, but they require a high frame-rate as a strict requirement for performance. This assumption is often violated in real-world applications, when models run on embedded devices, often at only a few frames per second. Videos at low frame-rate suffer from large object displacements. Here re-identification features may support to match large-displaced object detections, but current joint detection and re-identification formulations degrade the detector performance, as these two are contrasting tasks. In the real-world application having separate detector and re-id models is often not feasible, as both the memory and runtime effectively double. Towards robust long-term tracking applicable to reduced-computational-power devices, we propose the first joint optimization of detection, tracking and re-identification features for videos. Notably, our joint optimization maintains the detector performance, a typical multi-task challenge. At inference time, we leverage detections for tracking (tracking-by-detection) when the objects are visible, detectable and slowly moving in the image. We leverage instead re-identification features to match objects which disappeared (e.g. due to occlusion) for several frames or were not tracked due to fast motion (or low-frame-rate videos). Our proposed method reaches the state-of-the-art on MOT, it ranks 1st in the UA-DETRAC'18 tracking challenge among online trackers, and 3rd overall. (C) 2020 Elsevier B.V. All tights reserved.

关键词： detection Multi-object tracking Re-identification Online tracking by detection

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A New Framework of Moving Object tracking based on Object detection-tracking with Removal of Moving Features

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2020年第4期11卷 35-46页

作者： Ly Quoc Ngoc Nguyen Thanh Tin Le Bao Tuan VNUHCM Univ Sci Dept Comp Vis & Cognit Cybernet Ho Chi Minh City Vietnam

Object tracking (OT) on a Moving Camera so-called Moving Object tracking (MOT) is extremely vital in Computer Vision. While other conventional tracking methods based on fixed camera can only track the objects in its range, a moving camera can tackle this issue by following the objects. Moreover, single tracker is used widely to track object but it is not effective due to the moving camera because the challenges such as sudden movements, blurring, pose variation. The paper proposes a method inherited by tracking by detection approach. It integrates a single tracker with object detection method. The proposed tracking system can track object efficiency and effectively because object detection method can be used to find the tracked object again if the single tracker loses track. Three main contributions are presented in the paper as follow. First, the proposed Unified Visual based-MOT system can do the tasks such as Localization, 3D Environment Reconstruction and tracking based on Stereo Camera and Inertial Measurement Unit (IMU). Second, it takes into account camera motion and the moving objects to improve the precision rate in localization and tracking. Third, proposed tracking system based on integration of single tracker as Deep Particle Filter and Object detection as Yolov3. The overall system is tested on the dataset KITTI 2012, and it has achieved a good accuracy rate in real time.

关键词： Moving object tracking object detection camera localization 3D environment reconstruction tracking by detection

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Boosting Multi-Vehicle tracking with a Joint Object detection and Viewpoint Estimation Sensor

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SENSORS 2019年第19期19卷 4062-4062页

作者： Lopez-Sastre, Roberto J. Herranz-Perdiguero, Carlos Guerrero-Gomez-Olmedo, Ricardo Onoro-Rubio, Daniel Maldonado-Bascon, Saturnino Univ Alcala Dept Signal Theory & Commun GRAM Alcala De Henares 28805 Spain BBVA Next Technol Madrid 28050 Spain NEC Labs Europe Kurfursten Anlage 36 D-69115 Heidelberg Germany

In this work, we address the problem of multi-vehicle detection and tracking for traffic monitoring applications. We preset a novel intelligent visual sensor for tracking-by-detection with simultaneous pose estimation. Essentially, we adapt an Extended Kalman Filter (EKF) to work not only with the detections of the vehicles but also with their estimated coarse viewpoints, directly obtained with the vision sensor. We show that enhancing the tracking with observations of the vehicle pose, results in a better estimation of the vehicles trajectories. For the simultaneous object detection and viewpoint estimation task, we present and evaluate two independent solutions. One is based on a fast GPU implementation of a Histogram of Oriented Gradients (HOG) detector with Support Vector Machines (SVMs). For the second, we adequately modify and train the Faster R-CNN deep learning model, in order to recover from it not only the object localization but also an estimation of its pose. Finally, we publicly release a challenging dataset, the GRAM Road Traffic Monitoring (GRAM-RTM), which has been especially designed for evaluating multi-vehicle tracking approaches within the context of traffic monitoring applications. It comprises more than 700 unique vehicles annotated across more than 40.300 frames of three videos. We expect the GRAM-RTM becomes a benchmark in vehicle detection and tracking, providing the computer vision and intelligent transportation systems communities with a standard set of images, annotations and evaluation procedures for multi-vehicle tracking. We present a thorough experimental evaluation of our approaches with the GRAM-RTM, which will be useful for establishing further comparisons. The results obtained confirm that the simultaneous integration of vehicle localizations and pose estimations as observations in an EKF, improves the tracking results.

关键词： traffic monitoring sensor vehicle tracking vehicle detection tracking by detection viewpoint estimation smart city

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Learning Real-Time Perspective Patch Rectification

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INTERNATIONAL JOURNAL OF COMPUTER VISION 2011年第1期91卷 107-130页

作者： Hinterstoisser, Stefan Lepetit, Vincent Benhimane, Selim Fua, Pascal Navab, Nassir Tech Univ Munich D-85748 Munich Germany Ecole Polytech Fed Lausanne Comp Vis Lab CH-1015 Lausanne Switzerland

We propose two learning-based methods to patch rectification that are faster and more reliable than state-of-the-art affine region detection methods. Given a reference view of a patch, they can quickly recognize it in new views and accurately estimate the homography between the reference view and the new view. Our methods are more memory-consuming than affine region detectors, and are in practice currently limited to a few tens of patches. However, if the reference image is a fronto-parallel view and the internal parameters known, one single patch is often enough to precisely estimate an object pose. As a result, we can deal in real-time with objects that are significantly less textured than the ones required by state-of-the-art methods. The first method favors fast run-time performance while the second one is designed for fast real-time learning and robustness. However, they follow the same general approach: First, a classifier provides for every keypoint a first estimate of its transformation. Then, the estimate allows carrying out an accurate perspective rectification using linear predictors. The last step is a fast verification-made possible by the accurate perspective rectification-of the patch identity and its sub-pixel precision position estimation. We demonstrate the advantages of our approach on real-time 3D object detection and tracking applications.

关键词： Patch rectification tracking by detection Object recognition Online learning Real-time learning Pose estimation

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