Comparison of static background segmentation methods

In the case of a static or motion compensated camera, static background segmentation methods can be applied to segment the interesting foreground objects from the background. Although a lot of methods have been proposed, a general assessment of the state of the art is not available. An important issue is to compare various state of the art methods in terms of quality (accuracy) and computational complexity (time and memory consumption). A representative set of recent techniques is chosen, implemented and compared to each other. An extensive set of videos is used to achieve comprehensive results. Both indoor and outdoor videos with different environmental conditions are used. While visual analysis is used for subjective assessment of the quality, pixel based measures based on available ground truth data are used for the objective assessment. Furthermore the computational complexity is estimated by measuring the elapsed time and memory requirements of each algorithm. The paper summarizes the experiments and considers the assets and drawbacks of the various techniques. Moreover, it will give hints for selecting the optimal approach for a specific environment and directions for further research in this field.

[1]  Touradj Ebrahimi,et al.  Intuitive strategy for parameter setting in video segmentation , 2003, Visual Communications and Image Processing.

[2]  Jenq-Neng Hwang,et al.  Fast and automatic video object segmentation and tracking for content-based applications , 2002, IEEE Trans. Circuits Syst. Video Technol..

[3]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[4]  Andrew K. C. Wong,et al.  A new method for gray-level picture thresholding using the entropy of the histogram , 1985, Comput. Vis. Graph. Image Process..

[5]  Larry S. Davis,et al.  W/sup 4/: Who? When? Where? What? A real time system for detecting and tracking people , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[6]  Thierry Pun,et al.  A new method for grey-level picture thresholding using the entropy of the histogram , 1980 .

[7]  Touradj Ebrahimi,et al.  Accurate video object segmentation through change detection , 2002, Proceedings. IEEE International Conference on Multimedia and Expo.

[8]  Touradj Ebrahimi,et al.  Change detection based on color edges , 2001, ISCAS 2001. The 2001 IEEE International Symposium on Circuits and Systems (Cat. No.01CH37196).

[9]  Azriel Rosenfeld,et al.  Tracking Groups of People , 2000, Comput. Vis. Image Underst..

[10]  Alexandre R. J. François,et al.  Adaptive Color Background Modeling for Real-Time Segmentation of Video Streams* , 1999 .

[11]  Azriel Rosenfeld,et al.  Detection and location of people in video images using adaptive fusion of color and edge information , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[12]  Woontack Woo,et al.  A background subtraction for a vision-based user interface , 2003, Fourth International Conference on Information, Communications and Signal Processing, 2003 and the Fourth Pacific Rim Conference on Multimedia. Proceedings of the 2003 Joint.

[13]  Jun Shen,et al.  Motion detection in color image sequence and shadow elimination , 2004, IS&T/SPIE Electronic Imaging.