Pattern Recognition, International Conference on (2000)
Sept. 3, 2000 to Sept. 8, 2000
Ismail Haritaoglu , IBM Almaden Research
David Harwood , University of Maryland at College Park
Larry S. Davis , University of Maryland at College Park
We described fast background scene modeling and maintenance techniques for real time visual surveillance system for tracking people in an outdoor environment. It operates on monocular grayscale video imagery, or on video imagery from an infrared camera. The system learns and models background scene statistically to detect foreground objects, even when the background is not completely stationary (e.g. motion of tree branches) using shape and motion cues. In addition, a background maintenance model is proposed for preventing false positives, such as, illumination changes (the sun being blocked by clouds causing changes in brightness), or false negative, such as, physical changes (person detection while he is getting out of the parked car). Experimental results demonstrate robustness and real-time performance of the algorithm.
I. Haritaoglu, D. Harwood and L. S. Davis, "A Fast Background Scene Modeling and Maintenance for Outdoor Surveillance," Pattern Recognition, International Conference on(ICPR), Barcelona, Spain, 2000, pp. 4179.