title: Far-Infrared Based Pedestrian Detection for Driver-Assistance Systems Based on Candidate Filters, Gradient-Based Feature and Multi-Frame Approval Matching.
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.
authors.affiliation: School of Software Engineering, South China University of Technology, No. 382 Waihuan East Rd., Guangzhou 510006, China. w.guohuascut@gmail.com.
authors.forename: Guohua
authors.initials: G
authors.lastname: Wang
authors.affiliation: School of Software Engineering, South China University of Technology, No. 382 Waihuan East Rd., Guangzhou 510006, China. liuqiong@scut.edu.cn.
authors.forename: Qiong
authors.initials: Q
authors.lastname: Liu
keywords: advanced driver-assistance systems; candidate filters; far-infrared video; gradient-based feature; pedestrian detection