Foreground detection based on optical flow and background subtract
Wei Li, Xiaojuan Wu, Koichi Matsumoto, Hua-An Zhao · 2010
The foreground detection is a key processing in crowd motion analysis containing abnormal behavior detections and crowd density estimations. This paper proposes a new foreground detection approach called optical flow and background model (OFBM) based on Lucas-Kanade optical flow and Gaussian background model methods. This approach overcomes the shortages of optical flow and background subtract. Experimental results prove that the foreground obtained by OFBM is better than the others and gets the lowest error rate. Also, OFBM is very useful in crowd motion analysis.