Moving Object Segmentation Using Improved Running Gaussian Average Background Model

Moving object segmentation using Improved Running Gaussian Average Background Model (IRGABM) is proposed in this paper. Background subtraction for a relatively static background is a popular method for moving object segmentation in image sequences. However, there are some problems for the background subtraction method, such as the varying luminance effect, the background updating problem, and the noise effect. IRGABM has the advantages of fast computational speed and low memory requirement. Our study also shows its improvements on the above-mentioned problems. For the purpose of moving object segmentation, background updating time, auto-thresholding and shadow detection are also discussed in this paper.

[1]  Alex Pentland,et al.  Pfinder: Real-Time Tracking of the Human Body , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Rita Cucchiara,et al.  Improving shadow suppression in moving object detection with HSV color information , 2001, ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585).

[3]  D. Koller,et al.  Towards robust automatic traffic scene analysis in real-time , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.

[4]  Massimo Piccardi,et al.  Background subtraction techniques: a review , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[5]  Erlend Hodneland,et al.  Segmentation of Digital Images , 2003 .

[6]  Rita Cucchiara,et al.  Detecting Moving Objects, Ghosts, and Shadows in Video Streams , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Alex Pentland,et al.  A Bayesian Computer Vision System for Modeling Human Interactions , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  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).

[9]  Larry S. Davis,et al.  Non-parametric Model for Background Subtraction , 2000, ECCV.

[10]  Stuart J. Russell,et al.  Image Segmentation in Video Sequences: A Probabilistic Approach , 1997, UAI.

[11]  Mohan M. Trivedi,et al.  Analysis and detection of shadows in video streams: a comparative evaluation , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[12]  Adrian Hilton,et al.  A survey of advances in vision-based human motion capture and analysis , 2006, Comput. Vis. Image Underst..