Effective Multi-Object Tracking via Global Object Models and Object Constraint Learning

Yoo, Yong-Sang; Lee, Seong-Ho; Bae, Seung-Hwan

doi:10.3390/s22207943

Cited by 5 publications

(4 citation statements)

References 80 publications

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“…The multi-object tracking effect on the public datasets MOT16 and MOT17 is shown in Figures 7 and 8 Based on the above evaluation index analysis, this network has certain advantages over other advanced methods ('-' means that the data are not given in the original paper). On the MOT16 dataset, the accuracy of multi-object tracking is improved by 0.5%, 2.2%, 10.9%, 25.1%, and 11.0%, respectively, compared with FairMOT, LMOT, MOT_GM [22], CRF_RNN [23], and JDE [24] methods. Compared with JDE, MOT_GM, and CRF_RNN methods, IDF1 is increased by 16.4%, 1.3%, and 17.8%, respectively.…”

Section: Performance Comparison Experiments With Existing Methodsmentioning

confidence: 99%

Research on Pedestrian Multi-Object Tracking Network Based on Multi-Order Semantic Fusion

Liu,

Han

2023

WEVJ

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Aiming at the problem of insufficient tracking accuracy caused by object occlusion in the process of multi-object tracking, this paper proposes a multi-order semantic fusion pedestrian multi-object tracking network. Firstly, the feature pyramid attention module is used in the backbone network to enlarge the receptive field and obtain more abundant feature information to improve the detection accuracy of different scale objects. Secondly, a size-aware module is integrated into the pedestrian re-identification branch network to fuse semantic features from different resolutions and extract more basic pedestrian features, thereby improving the tracking accuracy. Finally, the detection head is reconstructed and the small object detection layer is fused to make the proposed network adapt to objects of different sizes. Experiments on the MOT16 and MOT17 datasets show that the multi-object tracking accuracy of the proposed network reaches 75.4% (MOT16) and 74.3% (MOT17), which effectively deals with the problem of low tracking accuracy caused by occlusion in the field of autonomous driving, and achieves good tracking results. The network proposed in this paper improves the tracking accuracy of pedestrians and provides a basis for further practical applications.

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Section: Performance Comparison Experiments With Existing Methodsmentioning

confidence: 99%

Research on Pedestrian Multi-Object Tracking Network Based on Multi-Order Semantic Fusion

Liu,

Han

2023

WEVJ

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“…This application is used for counting the incoming and outgoing visitors in the store to understand the peak traffic of visitors. The manager can directly analyze this traffic information, assisting in product placement or promoting the product at a specific location [25].…”

Section: Retail Usagementioning

confidence: 99%

Methodological Insights Towards Leveraging Performance in Video Object Tracking and Detection

Kurian

2023

IJACSA

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Video Object Detection and Tracking (VODT), one of its integral operations of surveillance system in present time, mechanizes a way to identify and track the target object autonomously and seamlessly within its visual field. However, the challenges associated with video feeding are immensely high, and the scene context is out of human control, posing an impediment to a successful model of VODT. The presented work has discussed about effectiveness of existing VODT approaches considering its identified taxonomies viz. satellite based, remote sensing-based, unmanned-based, Real-time Tracking based, behavioral analysis and event detection based, integration of multiple data sources, and privacy and ethics. Further, research trend associated with cumulative publications and evolving methods to realize the frequently used methodologies in VODT.Further, the results of review showcase that there is prominent research gap of manifold attributes that demands to be addressed for improving performance of VODT.

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“…Therefore, this paper adopts YOLOv5l as the detection input and utilizes the labels of MOT16 as the ground truth. We compare the performance of the proposed improved DeepSORT method with the original DeepSORT and the existing baseline algorithm [ 54 ] in this case.…”

Section: Experimental Evaluationmentioning

confidence: 99%

Achieving Adaptive Visual Multi-Object Tracking with Unscented Kalman Filter

Zhang

Yin²,

Deng³

et al. 2022

Sensors

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As an essential part of intelligent monitoring, behavior recognition, automatic driving, and others, the challenge of multi-object tracking is still to ensure tracking accuracy and robustness, especially in complex occlusion environments. Aiming at the issues of the occlusion, background noise, and motion state violent change for multi-object in a complex scene, an improved DeepSORT algorithm based on YOLOv5 is proposed for multi-object tracking to enhance the speed and accuracy of tracking. Firstly, a general object motion model is devised, which is similar to the variable acceleration motion model, and a multi-object tracking framework with the general motion model is established. Then, the latest YOLOv5 algorithm, which has satisfactory detection accuracy, is utilized to obtain the object information as the input of multi-object tracking. An unscented Kalman filter (UKF) is proposed to estimate the motion state of multi-object to solve nonlinear errors. In addition, the adaptive factor is introduced to evaluate observation noise and detect abnormal observations so as to adaptively adjust the innovation covariance matrix. Finally, an improved DeepSORT algorithm for multi-object tracking is formed to promote robustness and accuracy. Extensive experiments are carried out on the MOT16 data set, and we compare the proposed algorithm with the DeepSORT algorithm. The results indicate that the speed and precision of the improved DeepSORT are increased by 4.75% and 2.30%, respectively. Especially in the MOT16 of the dynamic camera, the improved DeepSORT shows better performance.

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Effective Multi-Object Tracking via Global Object Models and Object Constraint Learning

Cited by 5 publications

References 80 publications

Research on Pedestrian Multi-Object Tracking Network Based on Multi-Order Semantic Fusion

Research on Pedestrian Multi-Object Tracking Network Based on Multi-Order Semantic Fusion

Methodological Insights Towards Leveraging Performance in Video Object Tracking and Detection

Achieving Adaptive Visual Multi-Object Tracking with Unscented Kalman Filter

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