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Robust Object Tracking for Scale Changes  

Cheon, Gi-Hong (NHN Company)
Kang, Hang-Bong (Dept. Media Eng., Catholic University of Korea)
Publication Information
Abstract
Though conventional video surveillance systems such as CCTV depended on operators, recently developed intelligent surveillance systems no longer needed operators. However, these new intelligent surveillance systems have their own problems such as Occlusion, changing scale of target object, and affine. This paper handled information damage caused by changing the scale of the target object among other objects. Due to the change of the scale, the inaccurate information of target can be obtained when we update the background information. To handle this problem, we introduce a solution for information damage caused by problem of changing scale of target object located among other objects. Specifically, we suggest multi-stage sampling particle filter based advanced MSER for object tracking system. Through this method, the problem caused by changing scale of target can be avoided.
Keywords
Object tracking; particle filter; MSER;
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1 D. Comaniciu, V. Ramesh, and P. Meer, "Kernel-Based Object Tracking", IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 5, pp. 564-575, May 2003   DOI   ScienceOn
2 Katja Nummiaro, Esther Koller-Meier and Luc Van Gool. "A Color-based Particle Filter", in First International Workshop on Generative- Model-Based Vision(GMBV), 2002
3 M. Isard and A. Blake, "CONDENSATION: conditional density propagation for visual tracking," International Journal on Computer Vision, vol. 29, no. 1, pp. 5-28, 1998   DOI
4 Lowe, D. G., "Distinctive Image Features from Scale-Invariant Keypoints", International Journal of Computer Vision, 60, 2, pp. 91-110, 2004. Lindeberg   DOI
5 J. Matas, O. Chum, M. Urban, and T.Pajdla. "Robust wide baseline stereo from maximally stable extremal regions", In Proc. of British Machine Vision conference, pp. 384-396, 2002
6 Yong Rui and Yunqiang Chen. "Better Proposal Distributions: Object Tracking Using Unscented Particle Filter", In Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 786-793, 2001
7 W. Qu, D. Schonfeld, and M. Mohamed, "Real-time interactively distributed multi-object tracking using a magnetic-inertia potential model,", In Proc. of 10th IEEE International Conference on Computer Vision (ICCV), vol. 1, pp. 535-540, Beijing, China, October 2005
8 Yang, C., Duraiswami, R., Davis, R. "Fast Multiple Object Tracking via a Hierarchical Particle Filter", In Proc. of 10th IEEE International Conference on Computer Vision (ICCV), 2005
9 천기홍, 강행봉. "다중 물체 추적에서의 모션 히스토그램을 이용한 샘플 생성 기법", HCI, 2006
10 Ying Wu, Ting Yu, Gang Hua. "Tracking Appearances with Occlusions", In Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 789, 2003
11 Hang-Bong Kang and Kihong Chun. "Multiple Object Tracking Via Multi-layer Multi-modal Framework", SCIA 2007, LNCS 4522, pp.789-797, 2007
12 Merwe, R., Doucet, A., Freitas, N., and Wan, E., "The unscented particle filter", Technical Report CUED/F-INFENG/TR 380, Cambridge University Engineering Department, August 2000
13 Andrea Vedaldi. "An Implementation of Multi-Dimensional Maximally Stable Extremal regions", 2007
14 Tao Wang, Qian Diao, Yimin Zhang, Gang Song, Chunrong Lai, Gary Bradski. "A Dynamic Bayesian Network Approach to Multi-cue based Visual Tracking", In Proc. Of International Conference on Pattern Recognition (ICPR), pp. 167-170, 2004
15 M. Donoser and H. Bischof. "Efficient maximally stable extremal region. (MSER) tracking", In Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp.553-560, 2006
16 S. Kang, J. Paik, A. Koschan, B. Abidi, and M. Abidi, "Real-time video tracking using PTZ cameras," QCAV, pp.103-111,. 19-22 May 2003
17 천기홍, 강행봉. "동일한 다중 물체 추적 기법", 대한전자공학회. vol29, pp. 679-680, 2006
18 Bohyung Han, Ying Zhu, Dorin Comaniciu, Larry S. Davis, "Kernel-Based Bayesian Filtering for Object Tracking", In Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR). pp.227-234, 2005