Moving Object Tracking Using Multiple Views and Data Association

Abstract:

In this paper, a moving object tracking method using multiple views of the same scene taken by three cameras are presented. The object of interest is the pedestrian in the street. Unlike the single view method, the proposed multi-view method can effectively exploit extra information available in the other views when the object of interest in one of the views falls into an occlusion or clutter environment. Making a relation of an object detected in one view with the ones in the other views is critical and it is obtained by data association. The simulation results show a significant performance improvement over the conventional one.

• Periodical: Applied Mechanics and Materials (Volume 666)
• Pages: 226-229
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.666.226
• Online since: October 2014
• Authors: Young Shin Ahn, Ahmed Mohammed, Jaeho Choi*
• Keywords: Data Association, Moving Object Detection and Tracking, Multiple Views

References

[1] B. Han, L. Davis, Object tracking by adaptive feature extraction, International Conference on Image Processing (ICIP), vol. 3, pp.1501-1504 Oct. (2004).

DOI: 10.1109/icip.2004.1421349

[2] S. Mockel, F. Scherer, P. Schuster, Multi-sensor obstacle detection on railway tracks, Proc. of Intelligent Vehicles Symposium 2003, pp.42-46, June (2003).

DOI: 10.1109/ivs.2003.1212880

[3] Bi Song, A.T. Kamal, C. Soto, Chong Ding, J. A Farrell, A. K Roy-Chowdhury, Tracking and activity recognition through consensus in distributed camera networks, IEEE Trans. on Image Processing, vol. 19, no. 10, pp.2564-2579, Oct. (2010).

DOI: 10.1109/tip.2010.2052823

[4] P. Van Beek, A. Tekalp, N. Zhuang, I. Celasun, M. Xia, Hierarchical 2-D mesh representation, tracking, and compression for object-based video, IEEE Trans. On Circuits and Systems for Video Technology, vol. 9, no. 2, pp.353-369, March (1999).

DOI: 10.1109/76.752101

[5] Yaonan Wang, Qin Wan, Hongshan Yu, Effective method for tracking multiple objects in real-time visual surveillance systems, Journal of Systems Engineering and Electronics, vol. 20, no. 6, pp.1167-1178, Dec. (2009).

[6] Junda Zhu, Yuanwei Lao, Zheng, Y.F., Object tracking in structured environments for video surveillance applications, IEEE Trans. On Circuits and Systems for Video Technology, vol. 20, no. 2, pp.223-235, Feb. (2010).

DOI: 10.1109/tcsvt.2009.2031395

[7] Martin Hofmann, Daniel Wolf1, Gerhard Rigoll Hypergraphs for Joint Multi-View Reconstruction and Multi-Ob ect Tracking, CVPR (2013).

DOI: 10.1109/cvpr.2013.468

[8] H. Pirsiavash, D. Ramanan, and C. Fowlkes. Globally-optimal greedy algorithms for tracking a variable number of objects., In CVPR, pages 1201 –1208, june (2011).

DOI: 10.1109/cvpr.2011.5995604

[9] J. F. Henriques, R. Caseiro, and J. Batista. Globally optimal solution to multi-object tracking with merged measurements, In ICCV, pages 2470–2477. IEEE, (2011).

DOI: 10.1109/iccv.2011.6126532

[10] L. Zhang, Y. Li, and R. Nevatia. Global data association for multiobject tracking using network flows., In CVPR. IEEE Computer Society, (2008).

[11] Z. Zivkovic, Improved adaptive Gaussian mixture model for background subtraction, Proc. 17th Int. Conf. on Pattern Recognition (ICPR), Cambridge, UK, pp.28-33, August (2004).

DOI: 10.1109/icpr.2004.1333992

[12] Hartley and Zisserman, Multiple View Geometry in Computer Vision, Second Edition. (2004).