A New Change Detection Algorithm for Visual Surveillance System

This paper describes the implementation of a background subtraction approach for indoor surveillance applications and presents a novel change detection algorithm. This algorithm uses a color similarity metric that considers the information of magnitude and phase of the difference between the background model and each video frame, both represented in the RGB color space. One of the advantages of such combination is that both features, magnitude and phase, take into account the intensity and the chromaticity information from a color image. The approach was tested using the PETS2004 database obtaining acceptable results.

[1]  Janne Heikkilä,et al.  A real-time system for monitoring of cyclists and pedestrians , 2004, Image Vis. Comput..

[2]  Alex Pentland,et al.  Pfinder: real-time tracking of the human body , 1996, Proceedings of the Second International Conference on Automatic Face and Gesture Recognition.

[3]  Sidney S. Fels,et al.  Evaluation of Background Subtraction Algorithms with Post-Processing , 2008, 2008 IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance.

[4]  José María Martínez Sanchez,et al.  Comparative Evaluation of Stationary Foreground Object Detection Algorithms Based on Background Subtraction Techniques , 2009, 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance.

[5]  Takeo Kanade,et al.  Introduction to the Special Section on Video Surveillance , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Badrinath Roysam,et al.  Image change detection algorithms: a systematic survey , 2005, IEEE Transactions on Image Processing.

[7]  Benjamin Höferlin,et al.  Evaluation of background subtraction techniques for video surveillance , 2011, CVPR 2011.

[8]  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).

[9]  Svetha Venkatesh,et al.  Adaptive Model for Foreground Extraction in Adverse Lighting Conditions , 2004, PRICAI.

[10]  Rita Cucchiara,et al.  Detecting Moving Objects, Ghosts, and Shadows in Video Streams , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Larry S. Davis,et al.  View-based detection and analysis of periodic motion , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[12]  Jorge S. Marques,et al.  New Performance Evaluation Metrics for Object Detection Algorithms , 2004 .

[13]  Vittorio Murino,et al.  Background Subtraction for Automated Multisensor Surveillance: A Comprehensive Review , 2010, EURASIP J. Adv. Signal Process..

[14]  Chandrika Kamath,et al.  Robust techniques for background subtraction in urban traffic video , 2004, IS&T/SPIE Electronic Imaging.

[15]  Massimo Piccardi,et al.  Background subtraction techniques: a review , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[16]  Larry S. Davis,et al.  W/sup 4/: Who? When? Where? What? A real time system for detecting and tracking people , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[17]  Shireen Elhabian,et al.  Moving Object Detection in Spatial Domain using Background Removal Techniques - State-of-Art , 2008 .

[18]  Touradj Ebrahimi,et al.  Change detection based on color edges , 2001, ISCAS 2001. The 2001 IEEE International Symposium on Circuits and Systems (Cat. No.01CH37196).

[19]  D. Koller,et al.  Towards robust automatic traffic scene analysis in real-time , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.