Fast Pedestrian Detection Based on Adaboost and Probability Template Matching

摘要：

In this paper, we propose a real time pedestrian detection approach which consists of two levels: coarse detection and further validation. First, partial stages of cascaded Adaboost classifiers are adopted to detect the upper bodies and generate candidate regions with a high detection rate. In the second level, a probability template is proposed, based on which a template matching technique is used to further reject the negative candidates. All the parameters involved are learnt from the training samples automatically. Our experimental results verify that the proposed approach improves detection performance substantially, while maintaining a fast processing speed.

关键词： pedestrian detection Adaboost probability template matching

作者: Zhihui Hao Bo Wang Juyuan Teng

作者单位: School of Automation Beijing Institute of Technology Beijing, China

会议类型: 国际会议

会议名称: The 2nd IEEE International Conference on Advanced Computer Control(第二届先进计算机控制国际会议 ICACC 2010)

会议地点: 沈阳

会议语种:英文

页码: 390-394

在线出版日期: 2010-03-27（万方平台首次上网日期，不代表论文的发表时间）