Optical Camera Based Pedestrian Detection in Rainy Or Snowy Weather
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.
KeywordsFalse Alarm Candidate Selection Pedestrian Detection AdaBoost Algorithm Detection Speed
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- 2.Xu, Y.W., Cao, X.B., Qiao, H.: A low cost pedestrian detection system. IEEE WCICA (June 2006) (accepted)Google Scholar
- 3.Amnon, S., Yoram, G., Gaby, H.: Pedestrian Detection for Driving Assis-tance Systems - Single-frame Classification and System Level Performance. In: IEEE Intelligent Vehicles Symposium, pp.1–6 (2004)Google Scholar
- 4.Bertozzi, M., Broggi, A., Lasagni, A., Del Rose, M.: Infrared stereo vision-based pedestrian detection. In: IEEE Intelligent Vehicles Symposium, pp. 24–29 (2005)Google Scholar
- 6.Gavrila, D.M., Giebel, J., Munder, S.: Vision-based pedestrian detection: the PROTECTOR system. In: IEEE Intelligent Vehicles Symposium, pp. 13–18 (2004)Google Scholar
- 7.Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. In: Vitányi, P.M.B. (ed.) EuroCOLT 1995. LNCS, vol. 904, pp. 23–37. Springer, Heidelberg (1995)Google Scholar
- 8.Joachims, T.: Making large-scale SVM learning practical. In: Schölkopf, B., Burges, C.J.C., Smola, A.J. (eds.) Advances in Kernel Methods—Support Vector Learning, MIT Press, Cambridge (1998)Google Scholar