Release Time:2019-03-11  Hits:

Indexed by: Journal Article

Date of Publication: 2014-01-01

Journal: Pattern Recognition and Image Analysis

Included Journals: EI

Volume: 24

Issue: 1

Page Number: 93-101

ISSN: 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.