Scene segmentation based on IPCA for visual surveillance

This paper proposes a simple scene segmentation method based on incremental principal component analysis (IPCA). Instead of segmenting moving objects in a conventional frame by frame manner, the newly proposed method segments a scene into unchanged background zone (UBZ) and moving object zone (MOZ). As a result, moving objects normally appear in MOZs rather than UBZs, and therefore, detection and behaviours analysis can be performed in MOZs. In visual communication, UBZs do not need to be encoded and transmitted. Moreover, if an object is in UBZs, it can be linked to abnormal events. Experimental results demonstrate the contribution of the proposed method.

[1]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[2]  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).

[3]  Michael J. Black,et al.  EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation , 1996, International Journal of Computer Vision.

[4]  Xuelong Li,et al.  Insignificant shadow detection for video segmentation , 2005, IEEE Transactions on Circuits and Systems for Video Technology.

[5]  Xing Zhang,et al.  The HCM for perceptual image segmentation , 2008, Neurocomputing.

[6]  Xuelong Li,et al.  KPCA for semantic object extraction in images , 2008, Pattern Recognit..

[7]  Xuelong Li,et al.  Cast shadow detection in video segmentation , 2005, Pattern Recognit. Lett..

[8]  Danijel Skocaj,et al.  Incremental and robust learning of subspace representations , 2008, Image Vis. Comput..

[9]  Yongmin Li,et al.  On incremental and robust subspace learning , 2004, Pattern Recognit..

[10]  Xuelong Li,et al.  Gabor-Based Region Covariance Matrices for Face Recognition , 2008, IEEE Transactions on Circuits and Systems for Video Technology.

[11]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[12]  Wen Gao,et al.  Modeling Background and Segmenting Moving Objects from Compressed Video , 2008, IEEE Transactions on Circuits and Systems for Video Technology.

[13]  Juyang Weng,et al.  Candid Covariance-Free Incremental Principal Component Analysis , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Hanqing Lu,et al.  Improving kernel Fisher discriminant analysis for face recognition , 2004, IEEE Transactions on Circuits and Systems for Video Technology.

[15]  Huiyu Zhou,et al.  Level set image segmentation with Bayesian analysis , 2008, Neurocomputing.

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

[17]  Pang Yan-wei.  A Novel Gabor-LDA Based Face Recognition Method , 2006 .

[18]  Nenghai Yu,et al.  Adaptive color quantization based on perceptive edge protection , 2003, Pattern Recognit. Lett..