| Title: | OS33-1 A Visual Measurement Algorithm of Approaching Vehicle Speed Based on Deep Learning |
|---|---|
| Publication: | ICAROB2022 |
| Volume: | 27 |
| Pages: | 116-123 |
| ISSN: | 2188-7829 |
| DOI: | 10.5954/ICAROB.2022.OS33-1 |
| Author(s): | Zhu Yurong, Zhao Huailin, Liu Junjie, Zhang Jinping, Ji Xiaojun |
| Publication Date: | January 20, 2022 |
| Keywords: | Vehicle speed measurement, semantic segmentation, HOG feature extraction, SVM classification |
| Abstract: | With the urbanizational process expediting and the national economy developing rapidly and healthily, the amount of private cars is on the rise, and traffic accidents occur frequently due to speeding and other reasons, and the difficulty of traffic supervision has also increased. This topic will use semantic segmentation and feature extraction and matching. Based on the video data of the traffic surveillance camera, an algorithm is designed to quickly calculate the matching of feature points in adjacent frames with low computing power to achieve the calculation. The same vehicle moves within the two frames of the target, so as to calculate the speed of the vehicle. Firstly, performing semantic segmentation based on deep learning, we choose a fully convolutional network to achieve semantic segmentation of depth maps, and distinguish the picture's principal part. After that, we can realize features extraction and mapping. The HOG algorithm is used on the matching step, the target's relative movement is calculated based on these matched point pairs to measure the moving speed of the vehicle. The experiment and the test prove that the system can realize the efficient speed measurement of moving vehicles. |
| PDF File: | https://alife-robotics.co.jp/members2022/icarob/data/html/data/OS/OS33/OS33-1.pdf |
| Copyright: | © The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/ |
(c)2008 Copyright The Regents of ALife Robotics Corporation Ltd. All Rights Reserved.