Person Tracking Using Ankle-Level LiDAR Based on Enhanced DBSCAN and OPTICS
Person Tracking Using Ankle-Level LiDAR Based on Enhanced DBSCAN and OPTICS
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DOI：
10.1002/tee.23358
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发表时间：
2021-04-01
影响因子：
1
通讯作者：
Kobayashi, Yoshinori
中科院分区：
文献类型：
--
作者：
Hasan, Mahmudul;Hanawa, Junichi;Kobayashi, Yoshinori
沿着深度学习技术的进步，使用摄像机进行人员跟踪变得简单而准确。然而，隐私和安全问题不足以关注基于视觉的监控。人们可能无法容忍在我们的日常生活中到处安装监控摄像头。基于摄像头的系统在烟雾、雾或黑暗等异常情况下可能无法正常工作。为了科普这些问题，我们提出了一种基于聚类算法的二维（2D）激光雷达的人跟踪技术。LiDAR传感器是一种重要的方法，即使在具有挑战性的条件下，也可以在不透露身份的情况下跟踪人员。对于跟踪人，我们提出了改进的基于密度的空间聚类的应用程序与噪声（DBSCAN）和排序点，以确定集群结构（OPTICS）算法聚类二维激光雷达数据。我们已经证实，我们的方法显着提高了准确性和鲁棒性的人跟踪通过实验。(c)2021日本电气工程师学会。出版社：Wiley Periodicals LLC
Along with the progress of deep learning techniques, people tracking using video cameras became easy and accurate. However, privacy and security issues are not enough to be concerned with vision-based monitoring. People may not be tolerated surveillance cameras installed everywhere in our daily life. A camera-based system may not work robustly in unusual situations such as smoke, fogs, or darkness. To cope with these problems, we propose a two-dimensional (2D) LiDAR-based people tracking technique based on clustering algorithms. A LiDAR sensor is a prominent approach for tracking people without disclosing their identity, even under challenging conditions. For tracking people, we propose modified density-based spatial clustering of applications with noise (DBSCAN) and ordering points to identify cluster structure (OPTICS) algorithms for clustering 2D LiDAR data. We have confirmed that our approach significantly improves the accuracy and robustness of people tracking through the experiments. (c) 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.