文献类型：期刊文献

英文题名：Pedestrian Detection Based on HOG Features and SVM Realizes Vehicle-Human-Environment Interaction

作者：Nan, Ma[1]; Li, Chen[1]; Jiancheng, Hu[1]; Qiuna, Shang[1]; Jiahong, Li[1]; Guoping, Zhang[1]

第一作者：马楠

机构：[1] Software Engineering, Beijing Union University, Beijing Key Laboratory of Information Service Engineering, College of Robotics, Beijing, China

第一机构：北京联合大学机器人学院|北京联合大学北京市信息服务工程重点实验室

年份：2019

起止页码：287-291

外文期刊名：Proceedings - 2019 15th International Conference on Computational Intelligence and Security, CIS 2019

收录：EI(收录号：20201308355549)

语种：英文

外文关键词：Pedestrian safety - Statistical tests - Feature extraction - Autonomous vehicles - Classification (of information) - Support vector machines

摘要：Autonomous driving has to deal with human-vehicle interaction, in which one of the key tasks is to detect pedestrians. In this paper, HOG, a classical algorithm in the pedestrian detection field is used for extracting features and SVM for pedestrian classifier training. The pedestrian feature classifier is obtained through training and testing using INRIA pedestrian dataset and data acquired by autonomous vehicles. Meanwhile, we design a pedestrian detection visualization system for better application on autonomous vehicles to detecting pedestrian. This system detects the image data input by users and calls the pedestrian feature classifier that has been trained to effectively mark pedestrians. ? 2019 IEEE.