Indexed by:期刊论文

Date of Publication:2014-01-01

Journal:Pattern Recognition and Image Analysis

Included Journals:EI

Volume:24

Issue:1

Page Number:93-101

ISSN No.:10546618

Abstract: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.