Bypassing BigBackground: An efficient hybrid background modeling algorithm for embedded video surveillance

As computer vision algorithms move to embedded platforms within distributed smart camera systems, greater attention must be placed on the efficient use of storage and computational resources. Significant savings can be made in background modeling by identifying large areas that are homogenous in color and sparse in activity. This paper presents a pixel-based background model that identifies such areas, called BigBackground, from a single image frame for fast processing and efficient memory usage. We use a small 15 color palette to identify and represent BigBackground colors. Results on a variety of outdoor and standard test sequences show that our algorithm performs in real-time on an embedded processing platform (the eBox-2300) with reliable background/foreground segmentation accuracy.

[1]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  D.M. Mount,et al.  An Efficient k-Means Clustering Algorithm: Analysis and Implementation , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

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

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

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

[6]  Michael Gervautz,et al.  A simple method for color quantization: octree quantization , 1990 .

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

[8]  Antonio Gentile,et al.  Midground object detection in real world video scenes , 2007, 2007 IEEE Conference on Advanced Video and Signal Based Surveillance.

[9]  Paul S. Heckbert.  Color image quantization for frame buffer display , 1982, SIGGRAPH.

[10]  Jitendra Malik,et al.  Normalized Cuts and Image Segmentation , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  K. Shadan,et al.  Available online: , 2012 .

[12]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[13]  Trista Pei-chun Chen,et al.  Computer Vision Workload Analysis: Case Study of Video Surveillance Systems , 2005 .

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

[15]  Antonio Gentile,et al.  Multimodal Mean Adaptive Backgrounding for Embedded Real-Time Video Surveillance , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[17]  Jian Zhang,et al.  Detecting New Stable Objects In Surveillance Video , 2005, 2005 IEEE 7th Workshop on Multimedia Signal Processing.

[18]  Pavel Pudil,et al.  Introduction to Statistical Pattern Recognition , 2006 .