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.
Highlights
Pedestrian safety is an important problem for autonomous vehicles
We proposed a novel hierarchical features extraction and weighted kernel sparse representation (HFE − WKSR) model for pedestrian classification
We propose a WKSR model, which uses kernel representation to fully exploit the discrimination information embedded in the hierarchical local features, and adopts a Gaussian function as the measure to effectively handle the occlusion in query images
Summary
Pedestrian safety is an important problem for autonomous vehicles. A World Health Organization report describes road accidents as one of the significant causes of fatalities. After detecting the individual body parts, detection results are fused using latent SVM [32], a Mixture-of-Experts framework [33], and the Restricted Boltzmann Machine Model [34] These methods perform well under controlled conditions, they cannot handle effectively partially occluded, varying appearance and small-scale pedestrian images in a real-world scenario [2,35]. We proposed a novel hierarchical features extraction and weighted kernel sparse representation (HFE − WKSR) model for pedestrian classification. Compared with the previous classification methods, e.g., SVM with HOG features and SRC with holistic features, the proposed HFE − WKSR model shows much greater robustness.
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