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. 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.
Code (0)
등록된 구현이 없습니다.
Tasks
Anomaly DetectionVideo Anomaly DetectionSimilar Papers 제목 키워드 기반
HyPCV-Former: Hyperbolic Spatio-Temporal Transformer for 3D Point Cloud Video Anomaly Detection
Video anomaly detection is a fundamental task in video surveillance, with broad applications in public safety and intelligent monitoring systems. Although previous methods leverage Euclidean representations in RGB or dep…
Video Anomaly DetectionToward Unsupervised 3D Point Cloud Anomaly Detection using Variational Autoencoder
In this paper, we present an end-to-end unsupervised anomaly detection framework for 3D point clouds. To the best of our knowledge, this is the first work to tackle the anomaly detection task on a general object represen…
Anomaly DetectionUnsupervised Anomaly DetectionReal3D-AD: A Dataset of Point Cloud Anomaly Detection
High-precision point cloud anomaly detection is the gold standard for identifying the defects of advancing machining and precision manufacturing. Despite some methodological advances in this area, the scarcity of dataset…
3D Anomaly DetectionAnomaly DetectionBoosting Global-Local Feature Matching via Anomaly Synthesis for Multi-Class Point Cloud Anomaly Detection
Point cloud anomaly detection is essential for various industrial applications. The huge computation and storage costs caused by the increasing product classes limit the application of single-class unsupervised methods, …
3D Anomaly Detection3D Anomaly Detection and SegmentationAnomaly DetectionBoosting Global-Local Feature Matching via Anomaly Synthesis for Multi-Class Point Cloud Anomaly Detection
Point cloud anomaly detection is essential for various industrial applications. The huge computation and storage costs caused by the increasing product classes limit the application of single-class unsupervised methods, …
Anomaly Detection