Multi-person vision tracking approach based on human body localization features

doi:10.1007/s40436-021-00363-0

Advances in Manufacturing ›› 2021, Vol. 9 ›› Issue (4): 496-508.doi: 10.1007/s40436-021-00363-0

• ARTICLES • 上一篇

Multi-person vision tracking approach based on human body localization features

Ao-Lei Yang¹, Hai-Yan Ren¹, Min-Rui Fei¹, Wasif Naeem²

1 School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, People's Republic of China;
2 School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast BT7 1NN, Northern Ireland, UK

收稿日期:2020-12-15 修回日期:2021-03-16 发布日期:2021-11-12
通讯作者: Ao-Lei Yang E-mail:ayang02@qub.ac.uk
基金资助:
This work was supported by the Natural Science Foundation of Shanghai Municipality (Grant No. 18ZR1415100) and the National Natural Science Foundation of China (Grant No.61703262).

Multi-person vision tracking approach based on human body localization features

Ao-Lei Yang¹, Hai-Yan Ren¹, Min-Rui Fei¹, Wasif Naeem²

1 School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, People's Republic of China;
2 School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast BT7 1NN, Northern Ireland, UK

Received:2020-12-15 Revised:2021-03-16 Published:2021-11-12
Contact: Ao-Lei Yang E-mail:ayang02@qub.ac.uk
Supported by:
This work was supported by the Natural Science Foundation of Shanghai Municipality (Grant No. 18ZR1415100) and the National Natural Science Foundation of China (Grant No.61703262).

摘要/Abstract

摘要： This paper presents a multi-person vision tracking approach based on human body localization features to address the problem of interactive object localization and tracking in a home monitoring scenario. Firstly, the human body localization model is used to obtain the 3D position of the human body, which is then used to construct the human body motion model based on the Kalman filter method. At the same time, the human appearance model is constructed by fusing human color features and features of the histogram of oriented gradient to better characterize the human body. Secondly, the human body observation model is constructed based on the human body motion model and appearance model to measure the similarities between the human body state sequence in the historical frame and the human body observation result in the current frame, and the cost matrix is then obtained. Thirdly, the Hungarian maximum matching algorithm is employed to match each human body in the current and historical frames, and the exception detection mechanism is simultaneously constructed to further reduce the probability of human tracking and matching failure. Finally, a multi-person vision tracking verification platform was constructed, and the achieved average accuracy was 96.6% in the case of human body overlapping, occlusion, disappearance, and appearance; this verifies the feasibility and effectiveness of the proposed method.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-021-00363-0

关键词: Multi-person vision tracking, Human body positioning, Motion model, Body observation model

Abstract: This paper presents a multi-person vision tracking approach based on human body localization features to address the problem of interactive object localization and tracking in a home monitoring scenario. Firstly, the human body localization model is used to obtain the 3D position of the human body, which is then used to construct the human body motion model based on the Kalman filter method. At the same time, the human appearance model is constructed by fusing human color features and features of the histogram of oriented gradient to better characterize the human body. Secondly, the human body observation model is constructed based on the human body motion model and appearance model to measure the similarities between the human body state sequence in the historical frame and the human body observation result in the current frame, and the cost matrix is then obtained. Thirdly, the Hungarian maximum matching algorithm is employed to match each human body in the current and historical frames, and the exception detection mechanism is simultaneously constructed to further reduce the probability of human tracking and matching failure. Finally, a multi-person vision tracking verification platform was constructed, and the achieved average accuracy was 96.6% in the case of human body overlapping, occlusion, disappearance, and appearance; this verifies the feasibility and effectiveness of the proposed method.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-021-00363-0

Key words: Multi-person vision tracking, Human body positioning, Motion model, Body observation model

Ao-Lei Yang, Hai-Yan Ren, Min-Rui Fei, Wasif Naeem. Multi-person vision tracking approach based on human body localization features[J]. Advances in Manufacturing, 2021, 9(4): 496-508.

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Multi-person vision tracking approach based on human body localization features

Multi-person vision tracking approach based on human body localization features

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