Abstract: In order to detect pedestrians effectively, a multi-pose pedestrian detection method based on posterior multiple sparse dictionaries was proposed. Through pre-learning multiple different sparse dictionaries, and sparse coding the image, statistics for each dictionary corresponds to sparse coding histogram as the pedestrian image feature descriptor. The common information of multiple sparse dictionary features of all positive samples was obtained, and the feature of a single pedestrian sample was weighted, and the features of a posteriori multiple sparse dictionary could be obtained. Then pedestrians of different poses and views were divided into subclasses with clustering algorithm. A classifier was trained for each subclass. A multi-pose-view ensemble classifier was trained to combine the output values of different subclass classifiers with an equally weighted sum rule. Experimental results on different datasets suggest that the proposed posterior feature is more than the classical sparse dictionary and other typical features. Compared with the existing methods, by combining the posterior feature and the multi-pose-view ensemble classifier, the proposed method improves the detection accuracy effectively.

Key words: pedestrian detection, sparse dictionaries, feature extraction, posterior multiple sparse, multi-pose