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Optical Engineering

Far-infrared pedestrian detection for advanced driver assistance systems using scene context
Author(s): Guohua Wang; Qiong Liu; Qingyao Wu
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Paper Abstract

Pedestrian detection is one of the most critical but challenging components in advanced driver assistance systems. Far-infrared (FIR) images are well-suited for pedestrian detection even in a dark environment. However, most current detection approaches just focus on pedestrian patterns themselves, where robust and real-time detection cannot be well achieved. We propose a fast FIR pedestrian detection approach, called MAP-HOGLBP-T, to explicitly exploit the scene context for the driver assistance system. In MAP-HOGLBP-T, three algorithms are developed to exploit the scene contextual information from roads, vehicles, and background objects of high homogeneity, and we employ the Bayesian approach to build a classifier learner which respects the scene contextual information. We also develop a multiframe approval scheme to enhance the detection performance based on spatiotemporal continuity of pedestrians. Our empirical study on real-world datasets has demonstrated the efficiency and effectiveness of the proposed method. The performance is shown to be better than that of state-of-the-art low-level feature-based approaches.

Paper Details

Date Published: 21 April 2016
PDF: 18 pages
Opt. Eng. 55(4) 043105 doi: 10.1117/1.OE.55.4.043105
Published in: Optical Engineering Volume 55, Issue 4
Show Author Affiliations
Guohua Wang, South China Univ. of Technology (China)
Qiong Liu, South China Univ. of Technology (China)
Qingyao Wu, South China Univ. of Technology (China)


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