Browse > Article
http://dx.doi.org/10.3837/tiis.2015.06.014

Multiple Person Tracking based on Spatial-temporal Information by Global Graph Clustering  

Su, Yu-ting (School of Electronic Information Engineering, Tianjin University)
Zhu, Xiao-rong (School of Electronic Information Engineering, Tianjin University)
Nie, Wei-Zhi (School of Electronic Information Engineering, Tianjin University)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.9, no.6, 2015 , pp. 2217-2229 More about this Journal
Abstract
Since the variations of illumination, the irregular changes of human shapes, and the partial occlusions, multiple person tracking is a challenging work in computer vision. In this paper, we propose a graph clustering method based on spatio-temporal information of moving objects for multiple person tracking. First, the part-based model is utilized to localize individual foreground regions in each frame. Then, we heuristically leverage the spatio-temporal constraints to generate a set of reliable tracklets. Finally, the graph shift method is applied to handle tracklet association problem and consequently generate the completed trajectory for individual object. The extensive comparison experiments demonstrate the superiority of the proposed method.
Keywords
Multiple person tracking; object tracking; multiple cameras tracking; graph clustering;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 An-An Liu, Y-T Su, P-P Jia, Zan Gao, Tong Hao, and Z-X Yang, “Multipe/single-view human action recognition via part-induced multitask structural learning,” Cybernetics, IEEE Transactions on , pp. 1, 2014.
2 Zan Gao, Hua Zhang, An-An Liu, Yan-bing Xue, and Guang-ping Xu, “Human action recognition using pyramid histograms of oriented gradients and collaborative multi-task learning,” KSII Transactions on Internet and Information Systems (TIIS), vol. 8, no. 2, pp. 483–503, 2014.   DOI
3 An-An Liu, “Bidirectional integrated random fields for human behaviour understanding,” Electronics letters, vol. 48, no. 5, pp. 262–264, 2012.   DOI
4 An-An Liu, “Human action recognition with structured discriminative random fields,” Electronics letters, vol. 47, no. 11, pp. 651–653, 2011.   DOI
5 W. Nie, A. Liu, and Y. Su, "Multiple person tracking by spatiotemporal tracklet association," In AVSS, pp. 481-486, 2012.
6 An-An Liu, Kang Li, and Takeo Kanade, “A semi-markov model for mitosis segmentation in time-lapse phase contrast microscopy image sequences of stem cell populations,” Medical Imaging, IEEE Transactions on, vol. 31, no. 2, pp. 359– 369, 2012.   DOI
7 Weizhi Nie, Anan Liu, Yuting Su, Huanbo Luan, Zhaoxuan Yang, Liujuan Cao, and Rongrong Ji, “Single/cross-camera multiple-person tracking by graph matching,” Neurocomputing, vol. 139, pp. 220–232, 2014.   DOI
8 Z. Kalal, K. Mikolajczyk, and J. Matas, “Tracking-learning-detection,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 7, pp. 1409–1422, 2012.   DOI
9 J. Tu, H. Tao, and T. S. Huang. "Online updating appearance generative mixture model for meanshift tracking," In ACCV , vol. 1, pp. 694-703, 2006.
10 Z. Han, Q. Ye, and J. Jiao, "Online feature evaluation for object tracking using kalman filter," In ICPR, pp. 1-4, 2008.
11 L. Bazzani, M. Cristani, and V. Murino, "Decentralized particle filter for joint individual-group tracking," In CVPR, pp. 1886-1893, 2012.
12 Yaowen Guan, Xiaoou Chen, Deshun Yang, and Yuqian Wu, "Multi-person tracking-by-detection with local particle filtering and global occlusion handling," In IEEE International Conference on Multimedia and Expo, pp. 1-6, 2014.
13 Maha M. Azab, Howida A. Shedeed, and Ashraf Saad Hussein, “New technique for online object tracking-by-detection in video,” IET Image Processing, vol. 8, no. 12, pp. 794–803, 2014.   DOI
14 Pedro F. Felzenszwalb, David A. McAllester, and Deva Ramanan, "A discriminatively trained, multiscale, deformable part model," In 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2008.
15 Joao F. Henriques, Rui Caseiro, Pedro Martins, and Jorge Batista, "Exploiting the circulant structure of tracking-by-detection with kernels," In proc. of ECCV 2012, 12th European Conference on Computer Vision, pp. 702-715, 2012.
16 Arne Schumann, Martin Bauml, and Rainer Stiefelhagen, "Person tracking-by-detection with efficient selection of part-detectors," In 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2013, pp. 43-50, 2013.
17 Z. Gao, H. ZHang, G.P Xu, Y.B Xue, “Multi-perspective and Multi-modality Joint Representation and Recognition Model for 3D Action Recognition,” NeuroComputing, vol. 151, pp. 554–564, 2015.   DOI
18 G. Shu, A. Dehghan, O. Oreifej, E. Hand, and M. Shah, "Part-based multiple-person tracking with partial occlusion handling," In CVPR, pp. 1815-1821, 2012.
19 E. Piatkowska, A. N.Belbachir, S. Schraml, and M. Gelautz, "Spatiotemporal multiple persons tracking using dynamic vision sensor," In CVPR Workshops, pp. 35-40, 2012.
20 I. Zuriarrain, F. Lerasle, N. Arana-Arejolaleiba, and M. Devy, "An mcmc-based particle filter for multiple person tracking," In ICPR, pp. 1-4, 2008.
21 R. Munoz-Salinas, “A bayesian plan-view map based approach for multiple-person detection and tracking,” Pattern Recognition, vol. 41, no. 12, pp. 3665–3676, 2008.   DOI
22 Thomas Mauthner, Peter M. Roth, and Horst Bischof, "Learn to move: Activity specific motion models for tracking by detection," in Proc. of Computer Vision - ECCV 2012, pp.183-192, 2012.
23 Xiaoyan Jiang, Erik Rodner, and Joachim Denzler, "Multi-person trackingby-detection based on calibrated multi-camera systems," in Proc. of Computer Vision and Graphics - International Conference, pp. 743-751, 2012.
24 Zan Gao, Long-fei Zhang, Ming-yu Chen, Alexander Hauptmann, Hua Zhang, Anni Cai, “Enhanced and Hierarchical Structure Algorithm for Data Imbalance Problem in Semantic Extraction underMassive Video Dataset,”Multimedia Tools and Applications, vol. 68, no. 3, 2014.
25 N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," In CVPR, pp. 886-893, 2005.
26 M. Becha Kaaniche and F. Bremond, "Tracking hog descriptors for gesture recognition," In AVSS, pp. 140-145, 2009.
27 Michael Bredereck, Xiaoyan Jiang, Marco Korner, and Joachim Denzler, "Data association for multi-object tracking-by-detection in multi-camera networks," in Proc. of Sixth International Conference on Distributed Smart Cameras, pp. 1-6, 2012.
28 Z. Gao, H. Zhang, G.P Xu,Y.B Xue and A. G. Hauptmannc, “Multi-View Discriminative and Structured Dictionary Learning with Group Sparsity for Human Action Recognition,” Signal Processing, 2015.
29 B. Yang and R. Nevatia, "Multi-target tracking by online learning of nonlinear motion patterns and robust appearance models," In CVPR, pp. 1918-1925, 2012.
30 J. Berclaz, F. Fleuret, and P. Fua, "Robust people tracking with global trajectory optimization," In CVPR, pp. 744-750, 2006.
31 M. Patzold, R. Heras Evangelio, and T. Sikora, “Boosting multi-hypothesis tracking by means of instance-specific models,” AVSS, pp. 416–421, 2012.
32 Mohammad Nazmul Alam Khan, Guoliang Fan, Douglas R. Heisterkamp, and Liangjiang Yu, "Automatic target recognition in infrared imagery using dense HOG features and relevance grouping of vocabulary," in Proc. of IEEE Conference on Computer Vision and Pattern Recognition, pp. 293-298, 2014.
33 T. Gehrig and J. W. McDonough, “Tracking multiple speakers with probabilistic data association filters,” CLEAR, pp. 137–150, 2006.
34 H. Jiang, S. Fels, and J. J. Little, "A linear programming approach for multiple object tracking," In CVPR, 2007.
35 Liu Feng, Liu Xiaoyu, and Chen Yi, "An efficient detection method for rare colored capsule based on RGB and HSV color space," in Proc. of 2014 IEEE International Conference on Granular Computing, pp. 175-178, 2014.
36 S. Pellegrini, A. Ess, and L. J. Van Gool, "Improving data association by joint modeling of pedestrian trajectories and groupings," In ECCV, pp. 452-465, 2010.
37 P. Gorur and B. Amrutur, “Speeded up gaussian mixture model algorithm for background subtraction,” AVSS, pp. 386–391, 2011.
38 R. Kasturi, D. B. Goldgof, P. Soundararajan, and et al, “Framework for performance evaluation of face, text, and vehicle detection and tracking in video: Data, metrics, and protocol,” IEEE Trans. Pattern Anal, vol. 31, no. 2, pp. 319–336, 2009.   DOI
39 K. Yamaguchi, A. C. Berg, L. E. Ortiz, and T. L. Berg, "Who are you with and where are you going?" In CVPR, pp. 1345-1352, 2011.
40 L. Leal-Taixe, G. Pons-Moll, and B. Rosenhahn, "Everybody needs somebody: Modeling social and grouping behavior on a linear programming multiple people tracker," In ICCV Workshops, pp.120-127, 2011.
41 L. Zhang, Y. Li, and R. Nevatia, "Global data association for multi-object tracking using network flows," In CVPR, 2008.
42 B. Benfold and I. Reid, "Stable multi-target tracking in real-time surveillance video," In CVPR, pp. 3457-3464, 2011.