@inproceedings{1a16ee66f4984b5f9dd6d25394b8599d,
title = "A two-step pedestrian detection algorithm based on RGB-D data",
abstract = "A two-step pedestrian detection algorithm based RGB-D data is presented. Firstly, down-sample the depth image and extract the key information with a voxel grid. Second, remove the ground by using the random sample consensus (RANSAC) segmentation algorithm. Third, describe pedestrian characteristics by using Point Feature Histogram (PFH) and estimate the position of pedestrian preliminarily. Finally, calculate the pedestrian characteristics based on color image by Histogram of Oriented Gradient (HOG) descriptor and detect pedestrian using Support Vector Machine (SVM) classifier. The experimental results show that the algorithm can accurately detect the pedestrian not only in the single-pedestrian scene with pose variety but also in the multi-pedestrian scene with partial occlusion between pedestrians.",
keywords = "Down-sample, HOG, PFH, Pedestrian detection, RGB-D, SVM",
author = "Qiming Li and Liqing Hu and Yaping Gao and Yimin Chen and Lizhuang Ma",
note = "Publisher Copyright: {\textcopyright} IFIP International Federation for Information Processing 2017.; 2nd IFIP TC 12 International Conference on Intelligence Science, ICIS 2017 ; Conference date: 25-10-2017 Through 28-10-2017",
year = "2017",
doi = "10.1007/978-3-319-68121-4\_31",
language = "英语",
isbn = "9783319681207",
series = "IFIP Advances in Information and Communication Technology",
publisher = "Springer New York LLC",
pages = "288--294",
editor = "Zhongzhi Shi and Ben Goertzel and Jiali Feng",
booktitle = "Intelligence Science I - 2nd IFIP TC 12 International Conference, ICIS 2017, Proceedings",
}