Signal Processing Systems. 01/2011; 62:65-76.
EURASIP J. Image and Video Processing. 01/2011; 2011.
J. Mobile Multimedia. 01/2010; 6:170-184.
ABSTRACT: Automated video surveillance applications require accurate separation of foreground and background image content. Cost-sensitive
embedded platforms place real-time performance and efficiency demands on techniques to accomplish this task. In this chapter,
we evaluate pixel-level foreground extraction techniques for a low-cost integrated surveillance system. We introduce a new
adaptive background modeling technique, multimodal mean (MM), which balances accuracy, performance, and efficiency to meet
embedded system requirements. Our evaluation compares several pixel-level foreground extraction techniques in terms of their
computation and storage requirements, and functional accuracy for three representative video sequences. The proposed MM algorithm
delivers comparable accuracy of the best alternative (mixture of Gaussians) with a 6× improvement in execution time and an
18% reduction in required storage on an eBox-2300 embedded platform.
12/2008: pages 163-175;
Fourth IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2007, 5-7 September, 2007, Queen Mary, University of London, London, United Kingdom; 01/2007
2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2007), 18-23 June 2007, Minneapolis, Minnesota, USA; 01/2007
ABSTRACT: People naturally identify rapidly moving foreground and ignore persistent background. Identifying background pixels belonging to stable, chromatically clustered objects is important for efficient scene processing. This paper presents a technique that exploits this facet of human perception to improve performance and efficiency of background modeling on embedded vision platforms. Previous work on the Multimodal Mean (MMean) approach achieves high quality foreground extraction (comparable to Mixture of Gaussians (MoG)) using fast integer computation and a compact memory representation. This paper introduces a more efficient hybrid technique that combines MMean with palette-based background matching based on the chromatic distribution in the scene. This hybrid technique suppresses computationally expensive model update and adaptation, providing a 45% execution time speedup over MMean. It reduces model storage requirements by 58% over a MMean-only implementation. This background analysis enables higher frame rate, lower cost embedded vision systems.
Computer Vision and Image Understanding.