mlsanityData to 14 Oct 2026

An Exploratory Study on Human-Centric Video Anomaly Detection through Variational Autoencoders and Trajectory Prediction

arXiv 2406.15395 ↗First posted 29 Apr 2024cs.CV cs.AIcited by 19

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Abstract (arXiv)

Video Anomaly Detection (VAD) represents a challenging and prominent research task within computer vision. In recent years, Pose-based Video Anomaly Detection (PAD) has drawn considerable attention from the research community due to several inherent advantages over pixel-based approaches despite the occasional suboptimal performance. Specifically, PAD is characterized by reduced computational complexity, intrinsic privacy preservation, and the mitigation of concerns related to discrimination and bias against specific demographic groups. This paper introduces TSGAD, a novel human-centric Two-Stream Graph-Improved Anomaly Detection leveraging Variational Autoencoders (VAEs) and trajectory prediction. TSGAD aims to explore the possibility of utilizing VAEs as a new approach for pose-based human-centric VAD alongside the benefits of trajectory prediction. We demonstrate TSGAD's effectiveness through comprehensive experimentation on benchmark datasets. TSGAD demonstrates comparable results with state-of-the-art methods showcasing the potential of adopting variational autoencoders. This suggests a promising direction for future research endeavors. The code base for this work is available at https://github.com/TeCSAR-UNCC/TSGAD.

Key facts. Cited by 19 papers in the map (data through 14 Oct 2026). Builds on 22 of its 43 references that are in the map. First posted on arXiv 29 Apr 2024.

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22 of 43 references.

References · 22

  1. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,136
  2. 2016InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Netscited by 860
  3. 2018Real-world Anomaly Detection in Surveillance Videoscited by 577
  4. 2017Future Frame Prediction for Anomaly Detection -- A New Baselinecited by 391
  5. 2018Understanding disentangling in β-VAEcited by 335
  6. 2018CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmarkcited by 214
  7. 2020Anomaly Detection in Video via Self-Supervised and Multi-Task Learningcited by 148
  8. 2019Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videoscited by 104
  9. 2019Graph Embedded Pose Clustering for Anomaly Detectioncited by 95
  10. 2022Video Anomaly Detection by Solving Decoupled Spatio-Temporal Jigsaw Puzzlescited by 62
  11. 2019Multi-timescale Trajectory Prediction for Abnormal Human Activity Detectioncited by 59
  12. 2021A Hierarchical Spatio-Temporal Graph Convolutional Neural Network for Anomaly Detection in Videoscited by 28
10 more
  1. 2022Self-Supervised Learning for Anomalous Channel Detection in EEG Graphs: Application to Seizure Analysiscited by 28
  2. 2021Regularity Learning via Explicit Distribution Modeling for Skeletal Video Anomaly Detectioncited by 22
  3. 2022An Attribute-based Method for Video Anomaly Detectioncited by 21
  4. 2022A Survey of Graph-based Deep Learning for Anomaly Detection in Distributed Systemscited by 20
  5. 2022Context Recovery and Knowledge Retrieval: A Novel Two-Stream Framework for Video Anomaly Detectioncited by 18
  6. 2022CHAD: Charlotte Anomaly Datasetcited by 18
  7. 2023Ancilia: Scalable Intelligent Video Surveillance for the Artificial Intelligence of Thingscited by 17
  8. 2023Understanding the Challenges and Opportunities of Pose-based Anomaly Detectioncited by 15
  9. 2022Pishgu: Universal Path Prediction Network Architecture for Real-time Cyber-physical Edge Systemscited by 9
  10. 2018Moving Objects Analytics: Survey on Future Location & Trajectory Prediction Methodscited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 10; 2026: 7.

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  1. 2024Towards Adaptive Human-centric Video Anomaly Detection: A Comprehensive Framework and A New Benchmarkcited by 6
  2. 2024Human-Centric Video Anomaly Detection Through Spatio-Temporal Pose Tokenization and Transformercited by 6
  3. 2025Shopformer: Transformer-Based Framework for Detecting Shoplifting via Human Posecited by 6
  4. 2025Exploring Pose-Based Anomaly Detection for Retail Security: A Real-World Shoplifting Dataset and Benchmarkcited by 5
  5. 2025MoFM: A Large-Scale Human Motion Foundation Modelcited by 5
  6. 2025A Survey on Video Anomaly Detection via Deep Learning: Human, Vehicle, and Environmentcited by 4
  7. 2025ALFred: An Active Learning Framework for Real-world Semi-supervised Anomaly Detection with Adaptive Thresholdscited by 3
  8. 2026From Offline to Periodic Adaptation for Pose-Based Shoplifting Detection in Real-world Retail Securitycited by 3
  9. 2026Bounding-Box Trajectories Matter for Video Anomaly Detectioncited by 3
  10. 2025MoCLIP: Motion-Aware Fine-Tuning and Distillation of CLIP for Human Motion Generationcited by 2
  11. 2026Are Multimodal LLMs Ready for Surveillance? A Reality Check on Zero-Shot Anomaly Detection in the Wildcited by 2
  12. 2025Cerberus: Real-Time Video Anomaly Detection via Cascaded Vision-Language Modelscited by 1
7 more
  1. 2026From Frames to Events: Rethinking Evaluation in Human-Centric Video Anomaly Detectioncited by 1
  2. 2025Video Anomaly Detection with Contours -- A Studycited by 0
  3. 2025Vision-Based Mistake Analysis in Procedural Activities: A Review of Advances and Challengescited by 0
  4. 20253D Human-Human Interaction Anomaly Detectioncited by 0
  5. 2026BUSSARD: Normalizing Flows for Bijective Universal Scene-Specific Anomalous Relationship Detectioncited by 0
  6. 2026Reliability-Aware Prototype Calibration for Frozen Pose-Flow Video Anomaly Detectioncited by 0
  7. 2026VQ-VAD: Vector-quantized Motion Representation Learning for Human-centric Video Anomaly Detectioncited by 0