Statistical spatial multi-pixel-pair model for object detection

A novel robust model for background subtraction under complex scenes is proposed. Unlike the previous works, it utilizes multiple pixel-pairs which exhibit a stable co-occurrence relation. In training progress, the support pixels are screened by utilizing temporal covariance matrix, and the spatial distributions of support pixels are optimized by spatial sampling based on K-means clustering, in order to balance high co-occurrence and relaxed spatial distribution. Then in detection progress, with a parametrized condition, the background model performs robust and accurate detections, under two challenging datasets (PETS-2001 and AIST-INDOOR).

[1]  L. Davis,et al.  Background and foreground modeling using nonparametric kernel density estimation for visual surveillance , 2002, Proc. IEEE.

[2]  Nikos Paragios,et al.  Background modeling and subtraction of dynamic scenes , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[3]  Manabu Hashimoto,et al.  Extraction of unique pixels based on co-occurrence probability for high-speed template matching , 2010, 2010 International Symposium on Optomechatronic Technologies.

[4]  I. Haritaoglu,et al.  Background and foreground modeling using nonparametric kernel density estimation for visual surveillance , 2002 .

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

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

[7]  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).

[8]  Yaser Sheikh,et al.  Bayesian modeling of dynamic scenes for object detection , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[10]  W. Eric L. Grimson,et al.  Learning Patterns of Activity Using Real-Time Tracking , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Kazuhiko Sumi,et al.  Background subtraction based on cooccurrence of image variations , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[12]  Yutaka Satoh,et al.  Robust Background Subtraction Based on Statistical Reach Feature Method , 2009 .

[13]  Yutaka Satoh,et al.  Object detection based on a robust and accurate statistical multi-point-pair model , 2011, Pattern Recognit..

[14]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .