Point Cloud Video Anomaly Detection Based on Point Spatio-Temporal
  Auto-Encoder
Tengjiao He Wenguang Wang
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. Identifying
individuals through the point cloud is difficult due to its sparsity, which
protects personal privacy. In this study, we propose Point Spatio-Temporal
Auto-Encoder (PSTAE), an autoencoder framework that uses point cloud videos as
input to detect anomalies in point cloud videos. We introduce PSTOp and
PSTTransOp to maintain spatial geometric and temporal motion information in
point cloud videos. To measure the reconstruction loss of the proposed
autoencoder framework, we propose a reconstruction loss measurement strategy
based on a shallow feature extractor. Experimental results on the TIMo dataset
show that our method outperforms currently representative depth modality-based
methods in terms of AUROC and has superior performance in detecting Medical
Issue anomalies. These results suggest the potential of point cloud modality in
video anomaly detection. Our method sets a new state-of-the-art (SOTA) on the
TIMo dataset.