中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Pedestrian detection with local feature assistant

文献类型:会议论文

作者Xu, Y.W.; Cao, X.B.; Qiao, H.
出版日期2007
会议名称2007 IEEE International Conference on Control and Automation, ICCA 2007
会议日期30 May-1 June 2007
会议地点Guangzhou, China
关键词Pedestrain detection,Local feature,Adaboost algorithm
通讯作者Xu, Y.W.
英文摘要Until now, existing pedestrian detection systems usually use global features (e.g. appearance or motion) of human body to detect pedestrian; however, the detection rate needs to be improved in many situations since sometimes the global features can not be obtained. For example, a pedestrian may be partly covered by a car or his/her part may hide into the background. Therefore it is essential to adopt some local features of key parts of human body to assist pedestrian detection. In this paper, we propose a method using some key local features of human body to help pedestrian detection. Since the introduction of additional features will cost the system more time, in order to ensure the detection speed, we firstly use both appearance and motion global features of human body to select candidates, and then use local features of head and leg to do further confirmation. In the confirmation stage, we use three kinds of local features (head appearance, face color and hair color) to detect the head of each candidate; at the same time, we also choose some particular local appearance features to detect the leg. The experimental results indicate that this method can improve detection rate with almost the same detection speed; additionally, it can reduce false alarm sometimes.
会议录2007 IEEE International Conference on Control and Automation
源URL[http://ir.ia.ac.cn/handle/173211/12813]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_机器人应用与理论组
作者单位Dept. of Comput. Sci. & Technol., Univ. of Sci. & Technol. of China
推荐引用方式
GB/T 7714
Xu, Y.W.,Cao, X.B.,Qiao, H.. Pedestrian detection with local feature assistant[C]. 见:2007 IEEE International Conference on Control and Automation, ICCA 2007. Guangzhou, China. 30 May-1 June 2007.

入库方式: OAI收割

来源:自动化研究所

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