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CLC number: TP391

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Received: 2008-06-21

Revision Accepted: 2008-10-24

Crosschecked: 2009-04-27

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Journal of Zhejiang University SCIENCE A 2009 Vol.10 No.7 P.985~996

http://doi.org/10.1631/jzus.A0820474


Tracking multiple people under occlusion and across cameras using probabilistic models


Author(s):  Xuan-he WANG, Ji-lin LIU

Affiliation(s):  Institute of Information and Communication Engineering, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   xuanhewang@163.com

Key Words:  Color model, Motion model, Blob model, People occlusion, People tracking, Kernel density estimation


Xuan-he WANG, Ji-lin LIU. Tracking multiple people under occlusion and across cameras using probabilistic models[J]. Journal of Zhejiang University Science A, 2009, 10(7): 985~996.

@article{title="Tracking multiple people under occlusion and across cameras using probabilistic models",
author="Xuan-he WANG, Ji-lin LIU",
journal="Journal of Zhejiang University Science A",
volume="10",
number="7",
pages="985~996",
year="2009",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A0820474"
}

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%T Tracking multiple people under occlusion and across cameras using probabilistic models
%A Xuan-he WANG
%A Ji-lin LIU
%J Journal of Zhejiang University SCIENCE A
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%N 7
%P 985~996
%@ 1673-565X
%D 2009
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A0820474

TY - JOUR
T1 - Tracking multiple people under occlusion and across cameras using probabilistic models
A1 - Xuan-he WANG
A1 - Ji-lin LIU
J0 - Journal of Zhejiang University Science A
VL - 10
IS - 7
SP - 985
EP - 996
%@ 1673-565X
Y1 - 2009
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.A0820474


Abstract: 
Tracking multiple people under occlusion and across cameras is a challenging question for discussion. Furthermore, the cameras in this study are used to extend the field of view, which are distinguished from the same field of view. Such correspondence between multiple cameras is a burgeoning research subject in the area of computer vision. This paper effectively solves the problems of tracking multiple people who pass from one camera to another and segmenting people under occlusion using probabilistic models. The probabilistic models are composed of blob model, motion model and color model, which make the most of the space, motion and color information. First, we present a color model that uses maximum likelihood estimation based on non-parametric kernel density estimation. Second, we introduce a blob model based on mean shift, which segments the body into many regions according to the color of each person in order to spatially localize the color features corresponding to the way people are dressed. Clothes can be any mixture of colors. Third, we bring forward a motion model based on statistical probability which indicates the movement position of the same person between two successive frames in a single camera. Finally, we effectively unify the three models into a general probabilistic model and attain a maximization likelihood probability image, which is used to segment the foreground region under occlusion and to match people across multiple cameras.

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Reference

[1] Angel, D.S., Aifanti, N., Malassiotis, S., Strintzis, M.G., 2005. Prior knowledge based motion model representation. Electr. Lett. Comput. Vis. Image Anal., 5(3):55-67.

[2] Black, J., Ellis, T., Rosin, P., 2002. Multi-view Image Surveillance and Tracking. IEEE Workshop on Motion and Video Computing, p.169-174.

[3] Comaniciu, D., Meer, P., 1999. Mean Shift: analysis and Applications. IEEE Int. Conf. on Computer Vision, Kerkyra, Greece, p.1197-1203.

[4] Comaniciu, D., Meer, P., 2002. Mean shift: A robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell., 24(5):603-619.

[5] Cucchiara, R., Grana, C., Piccardi, M., Prati, A., 2003. Detecting moving objects, ghosts, and shadows in video streams. IEEE. Trans. Pattern Anal. Mach. Intell., 25(10):1337-1342.

[6] Du, W., Piater, J., 2007. Multi-camera People Tracking by Collaborative Particle Filters and Principal Axis-based Integration. Asian Conf. on Computer Vision, p.365-374.

[7] Duong, T., Hazelton, M.L., 2005. Cross-validation bandwidth matrices for multivariate kernel density estimation. Scandinav. J. Statist., 32(3):485-506.

[8] Fleuret, F., Berclaz, J., Lengagne, R., Fua, P., 2007. Multicamera people tracking with a probabilistic occupancy map. IEEE. Trans. Pattern Anal. Mach. Intell., 30(2):267-282.

[9] Giné, E., Koltchinskii, V., Zinn, J., 2004. Weighted uniform consistency of kernel density estimators. Inst. Math. Stat. Ann. Probab., 32(3B):2570-2605.

[10] Han, M., Xu, W., Tao, H., Gong, Y., 2004. An Algorithm for Multiple Object Trajectory Tracking. Conf. on Computer Vision and Pattern Recognition, 1:864-871.

[11] Isard, M., MacCormick, J., 2001. Bramble: A Bayesian Multiple-blob Tracker. Conf. on Computer Vision and Pattern Recognition, 2:34-41.

[12] Javed, O., Rasheed, Z., Shafique, K., Shah, M., 2003. Tracking Across Multiple Cameras with Disjoint Views. Proc. 9th IEEE Int. Conf. on Computer Vision, 2:952-957.

[13] Kang, J., Cohen, I., Medioni, G., 2004. Tracking People in Crowded Scenes Across Multiple Cameras. Asian Conf. on Computer Vision.

[14] Khan, S., Shah, M., 2000. Tracking People in Presence of Occlusion. Asian Conf. on Computer Vision.

[15] Khan, S., Shah, M., 2003. Consistent labeling of tracked objects in multiple cameras with overlapping field of view. IEEE Trans. Pattern Anal. Mach. Intell., 25(10):1355-1360.

[16] Khan, S., Shah, M., 2006. A Multiview Approach to Tracking People in Crowded Scenes Using a Planar Homography Constraint. Proc. European Conf. on Computer Vision.

[17] Lim, H., Morariu, V.I., Camps, O.I., Sznaier, M., 2006. Dynamic Appearance Modeling for Human Tracking. Proc. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition.

[18] Mittal, A., Davis, L.S., 2003. M2tracker: A multi-view approach to segmenting and tracking people in a cluttered scene. Int. J. Comput. Vis., 51(3):189-203.

[19] Otsuka, K., Mukawa, N., 2004. Multi-view Occlusion Analysis for Tracking Densely Populated Objects Based on 2-D Visual Angles. Conf. on Computer Vision and Pattern Recognition.

[20] Romano, R., Lee, L., Stein, G., 2000. Monitoring activities from multiple video streams: Establishing a common coordinate frame. IEEE Trans. PAMI, 22(8):758-768.

[21] Scott, D.W., Sain, S.R., 2004. Multi-dimensional Density Estimation. Elsevier Science.

[22] Smith, K., Gatica-Perez, D., Odobez, J.M., 2005. Using Particles to Track Varying Numbers of Interacting People. Proc. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, 1:962-969.

[23] Stauffer, C., Grimson, W.E.L., 1999. Adaptive Background Mixture Models for Real-time Tracking. Proc. IEEE Conf. on Computer Vision and Pattern Recognition.

[24] Sullivan, J., Blake, A., Isard, M., MacCormick, J., 1999. Object Localization by Bayesian Correlation. Proc. 7th Int. Conf. on Computer Vision, 2:1068-1075.

[25] Vega, I.R., Sarkar, S., 2003. Statistical motion model based on the change of feature relation ships: human gait-based recognition. IEEE Trans. Pattern Anal. Mach. Intell., 25(10):1323-1328.

[26] Wren, C.R., Pentland, A.P., 1998. Dynamic Modeling of Human Motion. Proc. 3rd IEEE Int. Conf. on Automatic Face and Gesture Recognition, p.22-27.

[27] Yu, Y., Harwood, D., Yoon, K., Davis, L.S., 2007. Human appearance modeling for matching across video sequences. Mach. Vis. Appl., 18(3-4):139-149.

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