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 neededfor 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.
A SVM-based classifier with shape and motion features for a pedestrian detection system
2006 IEEE Intelligent Vehicles Symposium ; 331-335
2006-01-01
1669818 byte
Conference paper
Electronic Resource
English
A SVM-based Classifier with Shape and Motion Features for a Pedestrian Detection System
British Library Conference Proceedings | 2006
|A motion and shape-based pedestrian detection algorithm
IEEE | 2003
|A Cascaded Classifier for Pedestrian Detection
IEEE | 2006
|A Cascaded Classifier for Pedestrian Detection
British Library Conference Proceedings | 2006
|Shape-based pedestrian detection
IEEE | 2000
|