Object tracking method using back-projection of multiple color histogram models

Automated object tracking system is needed for unmanned observing and proper recording of important places. In this paper, we propose an object tracking method which uses back-projection of color histogram with multiple models. We can make some representative models of an object from its color histogram distribution. 3D Labeling is introduced to eliminate unsuitable histogram blobs and color histogram models are composed from survived blobs. The position of interested object could be estimated with the back-projection image of each model. The proposed method can reduce the miss tracking even if object enters similar colored region in complex video scenes.

[1]  Gérard G. Medioni,et al.  Detecting and tracking moving objects for video surveillance , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[2]  Johnson I. Agbinya,et al.  Multi-Object Tracking in Video , 1999, Real Time Imaging.

[3]  Linda G. Shapiro,et al.  Computer and Robot Vision , 1991 .

[4]  Jake K. Aggarwal,et al.  Human Motion Analysis: A Review , 1999, Comput. Vis. Image Underst..

[5]  Jake K. Aggarwal,et al.  Human motion analysis: a review , 1997, Proceedings IEEE Nonrigid and Articulated Motion Workshop.

[6]  Rangachar Kasturi,et al.  Machine vision , 1995 .

[7]  Yoshiaki Shirai,et al.  Tracking players and a ball in soccer games , 1999, Proceedings. 1999 IEEE/SICE/RSJ. International Conference on Multisensor Fusion and Integration for Intelligent Systems. MFI'99 (Cat. No.99TH8480).

[8]  François Brémond,et al.  Tracking multiple nonrigid objects in video sequences , 1998, IEEE Trans. Circuits Syst. Video Technol..

[9]  Henry R. Kang.  Color Technology for Electronic Imaging Devices , 1997 .

[10]  W. Eric L. Grimson,et al.  Adaptive background mixture models for real-time tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[11]  Linda G. Shapiro,et al.  Computer Vision , 2001 .