title: Robust Pedestrian Classification Based on Hierarchical Kernel Sparse Representation.
abstract: Vision-based pedestrian detection has become an active topic in computer vision and autonomous vehicles. It aims at detecting pedestrians appearing ahead of the vehicle using a camera so that autonomous vehicles can assess the danger and take action. Due to varied illumination and appearance, complex background and occlusion pedestrian detection in outdoor environments is a difficult problem. In this paper, we propose a novel hierarchical feature extraction and weighted kernel sparse representation model for pedestrian classification. Initially, hierarchical feature extraction based on a CENTRIST descriptor is used to capture discriminative structures. A max pooling operation is used to enhance the invariance of varying appearance. Then, a kernel sparse representation model is proposed to fully exploit the discrimination information embedded in the hierarchical local features, and a Gaussian weight function as the measure to effectively handle the occlusion in pedestrian images. Extensive experiments are conducted on benchmark databases, including INRIA, Daimler, an artificially generated dataset and a real occluded dataset, demonstrating the more robust performance of the proposed method compared to state-of-the-art pedestrian classification methods.
authors.affiliation: School of Computer and Information, Hefei University of Technology, Tunxi Road 193, Hefei 230009, China. sunrui@hfut.edu.cn.
authors.forename: Rui
authors.initials: R
authors.lastname: Sun
authors.affiliation: School of Computer and Information, Hefei University of Technology, Tunxi Road 193, Hefei 230009, China. zhangghai@yeah.net.
authors.forename: Guanghai
authors.initials: G
authors.lastname: Zhang
authors.affiliation: Academy of Optoelectronic Technology, Hefei University of Technology, Tunxi Road 193, Hefei 230009, China. yxxing@hfut.edu.cn.
authors.forename: Xiaoxing
authors.initials: X
authors.lastname: Yan
authors.affiliation: School of Computer and Information, Hefei University of Technology, Tunxi Road 193, Hefei 230009, China. gaojun@hfut.edu.cn.
authors.forename: Jun
authors.initials: J
authors.lastname: Gao
mesh_terms: D000465:Algorithms; D001334:Automobile Driving; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D000069636:Pedestrians / Q000145:classification*
keywords: CENTRIST; kernel method; pedestrian classification; pooling; sparse representation