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论文类型：期刊论文

发表时间：2014-01-01

发表刊物：Pattern Recognition and Image Analysis

收录刊物：EI

卷号：24

期号：1

页面范围：93-101

ISSN号：10546618

摘要：Although a variety of promising approaches exist, it is still a hard work to obtain desirable results in the area of pedestrian detection, especially in crowded and cluttered scene. In this paper, we present a detector which includes a discriminative shape descriptor-Local Segmentation Self-Similarity (LSSS) and induces a simple but sophisticated sample strategy. The descriptor represents the local shape of the object based on saliency on log-polar coordinate. The image is divided into disjoint cells, and the AdaBoost algorithm is adopted to integrate the local shape feature into a simple and powerful classifier. In detecting step, a greedy procedure is utilized for eliminating the repeated detections via non-maximum suppression. Experiments show that our approach achieves the considerable improvements in dealing with heavy occlusion and mutative background. © 2014 Pleiades Publishing, Ltd.