Research of Pedestrian Detection for Intelligent Vehicle Based on Machine Vision
Efficiently and accurately detecting pedestrian plays a very important role in many computer vision applications such as Intelligent Transportation System and Safety Driving Assistant. This paper puts forwards a two-stage pedestrian detection method based on machine vision. Firstly, the expanded Haar-like characteristic is selected and calculated using integral map and the pedestrian detection cascaded classifiers with high accuracy are trained by Adaboost. After segmenting the candidate pedestrian areas from the image, a confirmation step is needed to judge whether those areas are pedestrian or not. Through analyzing the sample images, we can know that the gray image of pedestrian has some texture and gray symmetry features. In addition, the continuous edges of pedestrian make the extracted edges have certain boundary moments and gradient direction characters. Based on these features, each sample image is expressed by a multi-dimension characteristic vector. The final pedestrian classifier is obtained using support vector machines (SVM) training with the features abstracted above. The experiment results indicate that the algorithm could achieve effective recognition of vehicle proceeding pedestrians with different sizes, colors and shapes.
Guo Lie Zhang Mingheng Li Linhui Zhao Yibing Wang Rongben
School of Automotive Engineering,Dalian University of Technology,Dalian City,Liaoning Province,11602 Transportation College,Jilin University,Changchun City,Jilin Province,130025,China
国际会议
2009 IEEE International Conference on Robotics and Biomimetics(2009 IEEE 机器人与仿生技术国际会议 ROBIO 2009)
桂林
英文
1172-1177
2009-12-19（万方平台首次上网日期，不代表论文的发表时间）