Point Cloud Video Anomaly Detection Based on Point Spatio-Temporal Auto-Encoder

Video anomaly detection has great potential in enhancing safety in the production and monitoring of crucial areas. Currently, most video anomaly detection methods are based on RGB modality, but its redundant semantic information may breach the privacy of residents or patients. The 3D data obtained by depth camera and LiDAR can accurately locate anomalous events in 3D space while preserving human posture and motion information.

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Tengjiao He, Wenguang Wang (2024). Dataset: Point Cloud Video Anomaly Detection Based on Point Spatio-Temporal Auto-Encoder. https://doi.org/10.57702/6qe22hjy

DOI retrieved: December 16, 2024

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Created December 16, 2024
Last update December 16, 2024
Defined In https://doi.org/10.48550/arXiv.2306.04466
Author Tengjiao He
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Wenguang Wang
Homepage https://arxiv.org/abs/2205.13713