An Efficient Self-learning People Counting System

摘要：

People counting is a challenging task and has attracted much attention in the area of video surveillance. In this paper, we present an efficient self-learning people counting system which can count the exact number of people in a region of interest. This system based on bag-of-features model can effectively detect the pedestrians some of which are usually treated as background because they are static or move slowly. The system can also select pedestrian and non-pedestrian samples automatically and update the classifier in real-time to make it more suitable for certain specific scene. Experimental results on a practical public dataset named CASIA Pedestrian Counting Dataset show that the proposed people counting system is robust and accurate.

作者: Jingwen Li Lei Huang Changping Liu

作者单位: Institute of Automation,Chinese Academy of Sciences,Beijing 100190, China Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China

会议类型: 国际会议

会议名称: 第一届亚洲模式识别会议

会议地点: 北京

会议语种:英文

页码: 125-129

在线出版日期: 2011-11-28（万方平台首次上网日期，不代表论文的发表时间）