Hierarchical Method for Foreground Detection Using Codebook Model

This paper presents a hierarchical scheme with block-based and pixel-based codebooks for foreground detection. The codebook is mainly used to compress information to achieve a high efficient processing speed. In the block-based stage, 12 intensity values are employed to represent a block. The algorithm extends the concept of the block truncation coding, and thus it can further improve the processing efficiency by enjoying its low complexity advantage. In detail, the block-based stage can remove most of the backgrounds without reducing the true positive rate, yet it has low precision. To overcome this problem, the pixel-based stage is adopted to enhance the precision, which also can reduce the false positive rate. Moreover, the short-term information is employed to improve background updating for adaptive environments. As documented in the experimental results, the proposed algorithm can provide superior performance to that of the former related approaches.

[1]  Jörn Ostermann,et al.  Shadow detection for moving humans using gradient-based background subtraction , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[2]  Lucia Maddalena,et al.  Multivalued Background/Foreground Separation for Moving Object Detection , 2009, WILF.

[3]  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).

[4]  Yi-Ping Hung,et al.  Efficient hierarchical method for background subtraction , 2007, Pattern Recognit..

[5]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory , 1988 .

[6]  José Mira Mira,et al.  A new video segmentation method of moving objects based on blob-level knowledge , 2008, Pattern Recognit. Lett..

[7]  Tamás Szirányi,et al.  Bayesian Foreground and Shadow Detection in Uncertain Frame Rate Surveillance Videos , 2008, IEEE Transactions on Image Processing.

[8]  Nicolas Martel-Brisson,et al.  Learning and Removing Cast Shadows through a Multidistribution Approach , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Rita Cucchiara,et al.  Detecting Moving Objects, Ghosts, and Shadows in Video Streams , 2003, 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]  Kentaro Toyama,et al.  Wallflower: principles and practice of background maintenance , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[12]  Marko Heikkilä,et al.  A texture-based method for modeling the background and detecting moving objects , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Chung-Cheng Chiu,et al.  A Robust Object Segmentation System Using a Probability-Based Background Extraction Algorithm , 2010, IEEE Transactions on Circuits and Systems for Video Technology.

[14]  Lucia Maddalena,et al.  A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications , 2008, IEEE Transactions on Image Processing.

[15]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[16]  Wei Zhang,et al.  Moving Cast Shadows Detection Using Ratio Edge , 2007, IEEE Transactions on Multimedia.

[17]  Parvaneh Saeedi,et al.  Robust region-based background subtraction and shadow removing using color and gradient information , 2008, 2008 19th International Conference on Pattern Recognition.

[18]  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).

[19]  Vassilios Morellas,et al.  Robust Foreground Detection In Video Using Pixel Layers , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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