Embedded Real-Time Surveillance Using Multimodal Mean BackgroundModeling

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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.
Lingua originaleEnglish
Titolo della pubblicazione ospiteEmbedded Computer Vision
Pagine163-175
Numero di pagine12
Stato di pubblicazionePublished - 2008

Serie di pubblicazioni

NomeAdvances in pattern recognitio

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Gentile, A. (2008). Embedded Real-Time Surveillance Using Multimodal Mean BackgroundModeling. In Embedded Computer Vision (pagg. 163-175). (Advances in pattern recognitio).