A fast background scene modeling and maintenance for outdoor surveillance
作者： I. Haritaoglu , D. Harwood , L.S. Davis
关键词:
摘要: We describe fast background scene modeling and maintenance techniques for real time visual surveillance system tracking people in an outdoor environment. It operates on monocular gray scale video imagery or from infrared camera. The learns models statistically to detect foreground objects, even when the is not completely stationary (e.g. motion of tree branches) using shape cues. Also, a model proposed preventing false positives, such as, illumination changes (the sun being blocked by clouds causing brightness), negative, physical (person detection while he getting out parked car). Experimental results demonstrate robustness real-time performance algorithm.
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