A nonparametric approach to foreground detection in dynamic backgrounds

Foreground detection is a fundamental step in visual surveillance. However, accurate foreground detection is still a challenging task especially in dynamic backgrounds. In this paper, we present a nonparametric approach to foreground detection in dynamic backgrounds. It uses a history of recently pixel values to estimate background model. Besides, the adaptive threshold and spatial coherence are introduced to enhance robustness against false detections. Experimental results indicate that our approach achieves better performance in dynamic backgrounds compared with several approaches.