Abstract
This study explores the development of Human Activity Recognition (HAR) systems capable of identifying suspicious activities to enhance security in public spaces. We propose an innovative solution that integrates LiDAR sensors with deep learning technologies. Our method employs advanced models operating on LiDAR point cloud, PV-RCNN for human detection, and LidarGait++ for classifying activities into categories such as standing or walking (non-suspicious) and sneaking or fighting (suspicious). Due to the scarcity of suitable real-world datasets for training such systems, we utilize a 3D simulation tool, Blender, to create realistic environments and generate labeled point cloud data. This synthetic dataset allows us to train our models effectively under controlled yet near-realistic conditions. Preliminary results from this approach are promising, demonstrating accurate localization and classification of human activities and highlighting the potential of our system to contribute significantly to proactive security measures.
Recommended Citation
A. Aljumah et al., "LiDAR-based Framework for Detecting Suspicious Human Activities," Proceedings IEEE International Symposium on Circuits and Systems, pp. 1276 - 1280, Jan 2026.
The definitive version is available at https://doi.org/10.1109/ISCAS66217.2026.11562735
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
3D simulation; and deep learning; Human activity recognition; LiDAR sensors; point cloud processing
International Standard Serial Number (ISSN)
0271-4310
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2026, All rights reserved.
Publication Date
01 Jan 2026
Included in
Digital Communications and Networking Commons, Electrical and Computer Engineering Commons
