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Abstract
AbstractAlong 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.
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