@inproceedings{f04844e0c2294026b117706ac8398612,
title = "Research on Vehicle Forward Pedestrian Recognition Based on Multi-line LIDAR",
abstract = "Pedestrians are one of the most important elements in traffic scenes. In autopilot system, pedestrians need to be accurately detected. We found the latest research that 3D light detection and ranging (LIDAR) sensors can provide more accurate pedestrian location information. In this paper, the AdaBoost algorithm is used to improve the accuracy of the support vector machine (SVM) and high-accuracy pedestrian detection based on real-time 3D point cloud data. The first step is to process 3D points to 2D grid, followed by using k-means clustering algorithm to extract candidate points of the pedestrian. Next, nine features are chosen to train the SVM, the AdaBoost iterative process is used to reduce the further error rate to meet the classification requirements. This method has achieved significant progress in our experiment, the average classification accuracy has been improved to 92.4\% per scan.",
keywords = "AdaBoost, LIDAR, Pedestrian detection",
author = "Chenyang Guo and Guizhen Yu and Li Zhang and Huan Niu and Bin Zhou and Zhangyu Wang and Da Li",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Singapore Pte Ltd.; Chinese Intelligent Systems Conference, CISC 2019 ; Conference date: 26-10-2019 Through 27-10-2019",
year = "2020",
doi = "10.1007/978-981-32-9698-5\_59",
language = "英语",
isbn = "9789813296978",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Verlag",
pages = "529--538",
editor = "Yingmin Jia and Junping Du and Weicun Zhang",
booktitle = "Proceedings of 2019 Chinese Intelligent Systems Conference - Volume III",
address = "德国",
}