[1]YANG Huicheng,ZHU Wenbo,TONG Ying.Pedestrian collision warning system based on looking-in and looking-out visual information analysis[J].CAAI Transactions on Intelligent Systems,2019,14(4):752-760.[doi:10.11992/tis.201801016]
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Pedestrian collision warning system based on looking-in and looking-out visual information analysis

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 14 Number of periods: 2019 4 Page number: 752-760 Column: 学术论文—机器感知与模式识别 Public date: 2019-07-02
Title:
Pedestrian collision warning system based on looking-in and looking-out visual information analysis
Author(s):
YANG Huicheng;  ZHU Wenbo;  TONG Ying
College of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China
Keywords:
collision warning; internal and external information; pedestrian positioning; driver states; monocular vision; channel features; multi-task cascaded convolutional network; fuzzy inference system
CLC:
TP181
DOI:
10.11992/tis.201801016
Abstract:
Pedestrian collision warning systems usually provide early warning for drivers based on the technologies of pedestrian detection and collision time measurement. To provide a more reliable basis for risk assessment, a pedestrian collision warning method that involves analyzing the road condition and driver’s head pose simultaneously is proposed in this paper. Two monocular cameras are used to capture vehicle exterior and interior images, and a channel features detector is applied to locate pedestrians. The vertical and horizontal distances between pedestrians and ego-vehicle are estimated based on monocular vision distance measurement. The multi-task cascaded convolutional network is utilized for facial landmark detection. By solving a perspective-n-point (PnP) problem, the estimated head angles can reflect driver’s attention states. By combining both pedestrian location information and driver’s attention information, we implemented a fuzzy inference system to assess collision risk level. An experiment in real-world driving conditions demonstrated that the risk levels obtained from the fuzzy system are reliable and can provide guidance for collision avoidance.
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