Nighttime pedestrian detection with a normal camera using SVM classifier

Authors:
Qiming Tian;Hui Sun;Yupin Luo;Dongcheng Hu
Affiliations:
Department of Automation, Tsinghua University, Beijing, China;Department of Automation, Tsinghua University, Beijing, China;Department of Automation, Tsinghua University, Beijing, China;Department of Automation, Tsinghua University, Beijing, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Year:
2005

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Abstract

Accidents occurring at night and involving pedestrians represent a significant percentage of the total. This paper presents an approach for pedestrian detection in nighttime with a normal camera using a SVM classifier. Objects in the video are extracted with an adaptive threshold segmentation method at first. In the recognition phase, a preliminary classifier is used to discard most candidates and a SVM classifier is used in detailed shape analyzing. At last, a tracking module is used to verify the classification result. This approach is more cost-efficient than the previous approaches which are based on expensive infrared cameras. Experimental results show that the proposed approach can detect 71.26% pedestrians and run in real-time.