Abstract
Optical camera based detection method is a popular system to fulfill pedestrian detection; however, it is difficult to be used to detect pedestrians in complicated environment (e.g. rainy or snowy weather conditions). The difficulties mainly include: (1) The light is much weaker than in sunny days, therefore it is more difficult to design an efficient classification mechanism; (2) Since a pedestrian always be partly covered, only using its global features (e.g. appearance or motion) may be mis-detected; (3) The mirror images on wet road will cause a lot of false alarms. In this paper, based on our pervious work, we introduce a new system for pedestrian detection in rainy or snowy weather. Firstly, we propose a cascaded classification mechanism; and then, in order to improve detection rate, we adopt local appearance features of head, body and leg as well as global features. Besides that, a specific classifier is designed to detect mirror images in order to reduce false positive rate. The experiments in a single optical camera based pedestrian detection system show the effeteness of the proposed system.
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Xu, Y.W., Cao, X.B., Qiao, H. (2006). Optical Camera Based Pedestrian Detection in Rainy Or Snowy Weather. In: Wang, L., Jiao, L., Shi, G., Li, X., Liu, J. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2006. Lecture Notes in Computer Science(), vol 4223. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881599_147
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DOI: https://doi.org/10.1007/11881599_147
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45916-3
Online ISBN: 978-3-540-45917-0
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