A Background Reconstruction Algorithm Based on Pixel Intensity Classification

The background subtraction is an important method to detect the moving objects, and its difficulty is the background update. So a background reconstruction algorithm based on pixel intensity classification is presented in this paper. According to the hypothesis that the background pixel intensity appears in image sequence with maximum probability, the pixel intensity differences between sequential two frames are calculated, and the intensity values at the pixels are classified by means of these differences. For the new algorithm, neither the pre-training of the background without any moving target, nor the models of background and targets are needed. Simulation results indicate that background can be reconstructed correctly by using the new algorithm, so the target can be extracted perfectly and tracked successfully.

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

[2]  Yee-Hong Yang,et al.  Stationary background generation: An alternative to the difference of two images , 1990, Pattern Recognit..

[3]  Stephen M. Smith,et al.  ASSET-2: Real-Time Motion Segmentation and Shape Tracking , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  King Ngi Ngan,et al.  Automatic segmentation of moving objects for video object plane generation , 1998, IEEE Trans. Circuits Syst. Video Technol..

[5]  Alessandro Neri,et al.  Automatic moving object and background separation , 1998, Signal Process..

[6]  Hamid Aghajan,et al.  Video-based freeway-monitoring system using recursive vehicle tracking , 1995, Electronic Imaging.

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

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

[9]  Anil K. Jain,et al.  A background model initialization algorithm for video surveillance , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[10]  Wei-Yun Yau,et al.  An automatic drowning detection surveillance system for challenging outdoor pool environments , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[11]  Derek R. Magee,et al.  Tracking multiple vehicles using foreground, background and motion models , 2004, Image Vis. Comput..

[12]  Larry S. Davis,et al.  W4: Real-Time Surveillance of People and Their Activities , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Cen Feng,et al.  MULTI-DISTRIBUTION MODEL FOR BACKGROUND SUBTRACTION IN LONG-TERM VIDEO SURVEILLANCE SYSTEM , 2002 .

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

[15]  Carlos Orrite-Uruñuela,et al.  Detected motion classification with a double-background and a Neighborhood-based difference , 2003, Pattern Recognit. Lett..

[16]  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).

[17]  Anil K. Jain,et al.  Vehicle Segmentation and Classification Using Deformable Templates , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Nikos Paragios,et al.  Motion-based background subtraction using adaptive kernel density estimation , 2004, CVPR 2004.

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