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Sensors 2015, 15(12), 32188-32212; doi:10.3390/s151229874

Far-Infrared Based Pedestrian Detection for Driver-Assistance Systems Based on Candidate Filters, Gradient-Based Feature and Multi-Frame Approval Matching

School of Software Engineering, South China University of Technology, No. 382 Waihuan East Rd., Guangzhou 510006, China
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Author to whom correspondence should be addressed.
Academic Editor: Felipe Jimenez
Received: 13 October 2015 / Revised: 23 November 2015 / Accepted: 9 December 2015 / Published: 21 December 2015
(This article belongs to the Special Issue Sensors in New Road Vehicles)

Abstract

Far-infrared pedestrian detection approaches for advanced driver-assistance systems based on high-dimensional features fail to simultaneously achieve robust and real-time detection. We propose a robust and real-time pedestrian detection system characterized by novel candidate filters, novel pedestrian features and multi-frame approval matching in a coarse-to-fine fashion. Firstly, we design two filters based on the pedestrians’ head and the road to select the candidates after applying a pedestrian segmentation algorithm to reduce false alarms. Secondly, we propose a novel feature encapsulating both the relationship of oriented gradient distribution and the code of oriented gradient to deal with the enormous variance in pedestrians’ size and appearance. Thirdly, we introduce a multi-frame approval matching approach utilizing the spatiotemporal continuity of pedestrians to increase the detection rate. Large-scale experiments indicate that the system works in real time and the accuracy has improved about 9% compared with approaches based on high-dimensional features only. View Full-Text
Keywords: pedestrian detection; far-infrared video; advanced driver-assistance systems; gradient-based feature; candidate filters pedestrian detection; far-infrared video; advanced driver-assistance systems; gradient-based feature; candidate filters
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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MDPI and ACS Style

Wang, G.; Liu, Q. Far-Infrared Based Pedestrian Detection for Driver-Assistance Systems Based on Candidate Filters, Gradient-Based Feature and Multi-Frame Approval Matching. Sensors 2015, 15, 32188-32212.

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