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Person Tracking Using Ankle‐Level LiDAR Based on Enhanced DBSCAN and OPTICS
IEEJ Transactions on Electrical and Electronic Engineering ( IF 1 ) Pub Date : 2021-04-01 , DOI: 10.1002/tee.23358
Mahmudul Hasan 1, 2 , Junichi Hanawa 1 , Riku Goto 1 , Hisato Fukuda 1 , Yoshinori Kuno 1 , Yoshinori Kobayashi 1
Affiliation  

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. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

中文翻译:

使用基于增强型DBSCAN和OPTICS的踝关节水平LiDAR进行人员跟踪

随着深度学习技术的进步,使用摄像机进行跟踪的人们变得简单而准确。但是,隐私和安全问题不足以与基于视觉的监视相关。在我们的日常生活中,人们可能不会容忍安装在任何地方的监控摄像头。基于摄像头的系统在烟雾,雾气或黑暗之类的异常情况下可能无法正常运行。为了解决这些问题,我们提出了一种基于聚类算法的基于二维(2D)LiDAR的人员跟踪技术。LiDAR传感器是跟踪人员而不泄露其身份的一种重要方法,即使在严峻的条件下也是如此。为了追踪人物,我们提出了基于噪声的应用程序(DBSCAN)和排序点的改进的基于密度的空间聚类,以识别用于聚类2D LiDAR数据的聚类结构(OPTICS)算法。我们已经证实,我们的方法可以极大地提高人们通过实验进行跟踪的准确性和鲁棒性。©2021日本电气工程师学会。由Wiley Periodicals LLC发布。
更新日期:2021-04-22
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