TexCut: A Study of Background Subtraction by Texture Comparison on Graph Cut

We propose a novel method for background subtraction named “TexCut,” which extracts object regions precisely even when change in lighting condition is rapid and spatially inhomogeneous. Human’s cast shadows and secondary reflections affect lighting condition at a human-object interaction. Texture-based approach is known as a robust approach for such affection: however it fails in regions with homogenious color, and with inhomogenious affection in the lighting condition. TexCut deal with homogenious region by using graph cut for propagating the difference in neighbor textured regions. For the second problem, we use spatial smoothness of the afferction under a planer light source. We experimentally evaluated our method in several scenes with different lighting environments.

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

[2]  Larry S. Davis,et al.  Real-time foreground-background segmentation using codebook model , 2005, Real Time Imaging.

[3]  Ning Xu,et al.  Object segmentation using graph cuts based active contours , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[4]  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).

[5]  Oliver Schreer,et al.  Fast and robust shadow detection in videoconference applications , 2002, International Symposium on VIPromCom Video/Image Processing and Multimedia Communications.

[6]  Liyuan Li,et al.  Integrating intensity and texture differences for robust change detection , 2002, IEEE Trans. Image Process..

[7]  Paul L. Rosin Thresholding for change detection , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[8]  Loris Nanni,et al.  Local Ternary Patterns from Three Orthogonal Planes for human action classification , 2011, Expert Syst. Appl..

[9]  Kentaro Toyama,et al.  Wallflower: principles and practice of background maintenance , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.