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
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
.
DOI: 10.1007/s40436-021-00363-0
1. Li J, Zhan W, Hu Y et al (2020) Generic tracking and probabilistic prediction framework and its application in autonomous driving. IEEE Trans Intell Transp Syst 21(9):3634-3649
2. Fukunaga K, Hostetler LD (1975) The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Trans Inf Theory 21(1):32-40
3. Liu B, Huang J, Kulikowski C et al (2013) Robust visual tracking using local sparse appearance model and K-selection. IEEE Trans Pattern Anal Mach Intell 35(12):2968-2981
4. Elhoseny M (2019) Multi-object detection and tracking (MODT) machine learning model for real-time video surveillance systems. Circuits Syst Signal Process 39(2):611-630
5. Aftab W, Mihaylova L (2021) A learning Gaussian process approach for maneuvering target tracking and smoothing. IEEE Trans Aerosp Electron Syst 57(1):278-292
6. Zhang L, Li Y, Nevatia R (2008) Global data association for multi-object tracking using network flows. In:IEEE conference on computer vision and pattern recognition, Anchorage, USA, pp 23-28. https://doi.org/10.1109/CVPR.2008.4587584
7. Shitrit HB, Berclaz J, Fleuret F et al (2014) Multi-commodity network flow for tracking multiple people. IEEE Trans Pattern Anal Mach Intell 36(8):1614-1627
8. Zamir AR, Dehghan A, Shah M (2012) GMCP-tracker:global multi-object tracking using generalized minimum clique graphs. In:Fitzgibbon A, Lazebnik S, Perona P et al (eds) Computer vision-ECCV 2012:Lecture notes in computer science. Springer, Berlin. https://doi.org/10.1007/978-3-642-33709-3_25
9. Milan A, Roth S, Schindler K (2014) Continuous energy minimization for multitarget tracking. IEEE Trans Pattern Anal Mach Intell 36(1):58-72
10. Dehghan A, Assari SM, Shah M (2015) GMMCP tracker:globally optimal generalized maximum multi clique problem for multiple object tracking. In IEEE conference on computer vision and pattern recognition, Boston, USA, 7-12 June, pp 4091-4099. https://doi.org/10.1109/CVPR.2015.7299036
11. Zhou H, Ouyang WL, Cheng J et al (2019) Deep continuous conditional random fields with asymmetric inter-object constraints for online multi-object tracking. IEEE Trans Circuits Syst Video Technol 29(4):1011-1022
12. Xiang J, Sang N, Hou J et al (2016) Multitarget tracking using hough forest random field. IEEE Trans Circuits Syst Video Technol 26(11):2028-2042
13. Sun SJ, Akhtar N, Song HS et al (2021) Deep affinity network for multiple object tracking. IEEE Trans Pattern Anal Mach Intell 43(1):104-119
14. Ge Z, Chang F, Liu H (2017) Multi-target tracking based on Kalman filtering and optical flow histogram. In:2017 Chinese automation congress (CAC), Jinan, China, 20-22 Oct, pp 2540-2545. https://doi.org/10.1109/CAC.2017.8243203
15. Zhao Z, Yu S, Wu X (2009) A multi-target tracking algorithm using texture for real-time surveillance. In:IEEE international conference on robotics and biomimetics, Bangkok, Thailand, 22-25 Feb, pp 2150-2155. https://doi.org/10.1109/ROBIO.2009.4913335
16. Sheng H, Zhang Y, Chen J et al (2019) Heterogeneous association graph fusion for target association in multiple object tracking. IEEE Trans Circuits Syst Video Technol 29(11):3269-3280
17. Yang AL, Ren HY, Fei MR et al (2020) Dynamic body vision localization approach based on multiple regression. Chinese Journal of Scientific Instrument 41(7):252-260
18. Xu Y, Yu G, Wang Y et al (2016) A hybrid vehicle detection method based on Viola-Jones and HOG+SVM from UAV Images. Sensors 16(8):1325-1347
