Foreground Detection Based on Optical Flow and Background Subtract

摘要：

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.

作者: Wei LI Xiaojuan WU Koichi MATSUMOTO Hua-An ZHAO

作者单位: School of Information Science and Engineering, Shandong University, Shandong, 250100, China Dept. of Computer Science and Electrical Engineering, Kumamoto University, Kumamoto, 860-8555, Japan

会议类型: 国际会议

会议名称: 2010 International Conference on Communications,Circuits and Systems(2010年通信、电路与系统国际会议)

会议地点: 成都

会议语种:英文

页码: 359-362

在线出版日期: 2010-06-28（万方平台首次上网日期，不代表论文的发表时间）