mlsanityData to 7 Oct 2026

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

arXiv 2306.04466 ↗First posted 4 Jun 2023cs.CV eess.SPcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 34 of its 53 references that are in the map. First posted on arXiv 4 Jun 2023.

Builds on

34 of 53 references.

References · 34

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  2. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
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  4. 2019Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detectioncited by 560
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  9. 2020FLOT: Scene Flow on Point Clouds Guided by Optimal Transportcited by 129
  10. 2019MeteorNet: Deep Learning on Dynamic 3D Point Cloud Sequencescited by 117
  11. 2018Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in Videocited by 110
  12. 2019Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videoscited by 103
22 more
  1. 2022Generative Cooperative Learning for Unsupervised Video Anomaly Detectioncited by 102
  2. 2020A Survey of Single-Scene Video Anomaly Detectioncited by 88
  3. 2023Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detectioncited by 74
  4. 2019Identifying Unknown Instances for Autonomous Drivingcited by 73
  5. 2022PSTNet: Point Spatio-Temporal Convolution on Point Cloud Sequencescited by 68
  6. 2022Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptorscited by 66
  7. 2017Abnormal Event Detection in Videos using Spatiotemporal Autoencodercited by 57
  8. 20203DV: 3D Dynamic Voxel for Action Recognition in Depth Videocited by 41
  9. 2022Point Primitive Transformer for Long-Term 4D Point Cloud Video Understandingcited by 35
  10. 2021An Application-Driven Conceptualization of Corner Cases for Perception in Highly Automated Drivingcited by 33
  11. 2022Complete-to-Partial 4D Distillation for Self-Supervised Point Cloud Sequence Representation Learningcited by 30
  12. 2022Open-world Semantic Segmentation for LIDAR Point Cloudscited by 29
  13. 2021Spatial-Temporal Transformer for 3D Point Cloud Sequencescited by 22
  14. 2023Toward Unsupervised 3D Point Cloud Anomaly Detection using Variational Autoencodercited by 18
  15. 2021HandFoldingNet: A 3D Hand Pose Estimation Network Using Multiscale-Feature Guided Folding of a 2D Hand Skeletoncited by 16
  16. 2022Skeletal Video Anomaly Detection using Deep Learning: Survey, Challenges and Future Directionscited by 14
  17. 2022A Survey of Visual Sensory Anomaly Detectioncited by 11
  18. 2021TIMo -- A Dataset for Indoor Building Monitoring with a Time-of-Flight Cameracited by 9
  19. 2022MAPLE: Masked Pseudo-Labeling autoEncoder for Semi-supervised Point Cloud Action Recognitioncited by 8
  20. 2022Unsupervised Anomaly Detection from Time-of-Flight Depth Imagescited by 6
  21. 2021Motion Projection Consistency Based 3D Human Pose Estimation with Virtual Bones from Monocular Videoscited by 5
  22. 2022Composite Convolution: a Flexible Operator for Deep Learning on 3D Point Cloudscited by 4

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  1. 2025HyPCV-Former: Hyperbolic Spatio-Temporal Transformer for 3D Point Cloud Video Anomaly Detectioncited by 1