A SVM-based classifier with shape and motion features for a pedestrian detection system
Chen, D.; Cao, X. B.; Xu, Y. X.; Qiao, H.; Wang, F. Y.
2006
会议名称IEEE Intelligent Vehicles Symposium
会议录名称2006 IEEE INTELLIGENT VEHICLES SYMPOSIUM
会议日期JUN 13-15, 2006
会议地点Meguroku, JAPAN
摘要The most critical requirement of a pedestrian detection system is to quickly recognize pedestrians in an image. However, the huge number of candidate regions and the complexity of scenes usually make the recognition slow and unreliable. An efficient classifier is needed for a pedestrian detection system. In this paper, a decomposed SVM algorithm is used to train a classifier for pedestrian detection. The algorithm is stable and suitable for training a classifier with a large number of samples and the derived classifier is very efficient. Meanwhile, considering that our system is based on a single camera and the scenes are always complex, it is difficult to train a good classifier only with shape features. To solve these problems, we integrate shape information with motion information to compose a feature set and use it to train a classifier. Experiments show that our system based on this classifier works very well. Furthermore, we discuss the effect of applying motion features. With a proper percentage, motion features will be a good complement of the shape features in complex. scenes. Comparison between application of shape features and application of both shape and motion features shows the advantage of our method.
关键词Image Classification / Image Motion Analysis / Object Detection / Support Vector Machines / Traffic Engineering Computing / Svm-based Classifier / Decomposed Svm Algorithm / Motion Features / Pedestrian Detection System / Shape Features
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/12859
专题复杂系统管理与控制国家重点实验室_机器人理论与应用
通讯作者Chen, D.
作者单位Univ Sci & Technol China, Dept Comp Sci & Technol
推荐引用方式
GB/T 7714
Chen, D.,Cao, X. B.,Xu, Y. X.,et al. A SVM-based classifier with shape and motion features for a pedestrian detection system[C],2006.
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