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Sensors 2016, 16(8), 1296; doi:10.3390/s16081296

Robust Pedestrian Classification Based on Hierarchical Kernel Sparse Representation

School of Computer and Information, Hefei University of Technology, Tunxi Road 193, Hefei 230009, China
Academy of Optoelectronic Technology, Hefei University of Technology, Tunxi Road 193, Hefei 230009, China
Author to whom correspondence should be addressed.
Academic Editor: Felipe Jimenez
Received: 19 May 2016 / Revised: 9 August 2016 / Accepted: 10 August 2016 / Published: 16 August 2016
(This article belongs to the Special Issue Sensors for Autonomous Road Vehicles)
View Full-Text   |   Download PDF [1673 KB, uploaded 16 August 2016]   |  


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. View Full-Text
Keywords: pedestrian classification; CENTRIST; kernel method; sparse representation; pooling pedestrian classification; CENTRIST; kernel method; sparse representation; pooling

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Sun, R.; Zhang, G.; Yan, X.; Gao, J. Robust Pedestrian Classification Based on Hierarchical Kernel Sparse Representation. Sensors 2016, 16, 1296.

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