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.
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Tian, Q., Sun, H., Luo, Y., Hu, D. (2005). Nighttime Pedestrian Detection with a Normal Camera Using SVM Classifier. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_30
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DOI: https://doi.org/10.1007/11427445_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25913-8
Online ISBN: 978-3-540-32067-8
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