mlsanityData to 7 Oct 2026

CHAD: Charlotte Anomaly Dataset

arXiv 2212.09258 ↗First posted 19 Dec 2022cs.CV cs.AIcited by 18

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Abstract (arXiv)

In recent years, we have seen a significant interest in data-driven deep learning approaches for video anomaly detection, where an algorithm must determine if specific frames of a video contain abnormal behaviors. However, video anomaly detection is particularly context-specific, and the availability of representative datasets heavily limits real-world accuracy. Additionally, the metrics currently reported by most state-of-the-art methods often do not reflect how well the model will perform in real-world scenarios. In this article, we present the Charlotte Anomaly Dataset (CHAD). CHAD is a high-resolution, multi-camera anomaly dataset in a commercial parking lot setting. In addition to frame-level anomaly labels, CHAD is the first anomaly dataset to include bounding box, identity, and pose annotations for each actor. This is especially beneficial for skeleton-based anomaly detection, which is useful for its lower computational demand in real-world settings. CHAD is also the first anomaly dataset to contain multiple views of the same scene. With four camera views and over 1.15 million frames, CHAD is the largest fully annotated anomaly detection dataset including person annotations, collected from continuous video streams from stationary cameras for smart video surveillance applications. To demonstrate the efficacy of CHAD for training and evaluation, we benchmark two state-of-the-art skeleton-based anomaly detection algorithms on CHAD and provide comprehensive analysis, including both quantitative results and qualitative examination. The dataset is available at https://github.com/TeCSAR-UNCC/CHAD.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 17 of its 35 references that are in the map. First posted on arXiv 19 Dec 2022.

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17 of 35 references.

References · 17

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  3. 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,624
  4. 2019Deep High-Resolution Representation Learning for Human Pose Estimationcited by 1,339
  5. 2017Simple Online and Realtime Tracking with a Deep Association Metriccited by 908
  6. 2018Real-world Anomaly Detection in Surveillance Videoscited by 570
  7. 2017Future Frame Prediction for Anomaly Detection -- A New Baselinecited by 391
  8. 2020Not only Look, but also Listen: Learning Multimodal Violence Detection under Weak Supervisioncited by 235
  9. 2021Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learningcited by 226
  10. 2019Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videoscited by 103
  11. 2019Graph Embedded Pose Clustering for Anomaly Detectioncited by 95
  12. 2021UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detectioncited by 91
5 more
  1. 2019Street Scene: A new dataset and evaluation protocol for video anomaly detectioncited by 89
  2. 2019Multi-timescale Trajectory Prediction for Abnormal Human Activity Detectioncited by 59
  3. 2017Training Adversarial Discriminators for Cross-channel Abnormal Event Detection in Crowdscited by 28
  4. 2019RobustTP: End-to-End Trajectory Prediction for Heterogeneous Road-Agents in Dense Traffic with Noisy Sensor Inputscited by 16
  5. 2022ADG-Pose: Automated Dataset Generation for Real-World Human Pose Estimationcited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 5; 2025: 5; 2026: 6.

18 papers.

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  1. 2024An Exploratory Study on Human-Centric Video Anomaly Detection through Variational Autoencoders and Trajectory Predictioncited by 19
  2. 2023Understanding the Challenges and Opportunities of Pose-based Anomaly Detectioncited by 14
  3. 2024Evaluating the Effectiveness of Video Anomaly Detection in the Wild: Online Learning and Inference for Real-world Deploymentcited by 7
  4. 2024Towards Adaptive Human-centric Video Anomaly Detection: A Comprehensive Framework and A New Benchmarkcited by 6
  5. 2024Human-Centric Video Anomaly Detection Through Spatio-Temporal Pose Tokenization and Transformercited by 6
  6. 2025Exploring Pose-Based Anomaly Detection for Retail Security: A Real-World Shoplifting Dataset and Benchmarkcited by 5
  7. 2023A POV-based Highway Vehicle Trajectory Dataset and Prediction Architecturecited by 4
  8. 2024PV-VTT: A Privacy-Centric Dataset for Mission-Specific Anomaly Detection and Natural Language Interpretationcited by 4
  9. 2025A Survey on Video Anomaly Detection via Deep Learning: Human, Vehicle, and Environmentcited by 4
  10. 2025ALFred: An Active Learning Framework for Real-world Semi-supervised Anomaly Detection with Adaptive Thresholdscited by 3
  11. 2026From Offline to Periodic Adaptation for Pose-Based Shoplifting Detection in Real-world Retail Securitycited by 3
  12. 2025Multi Camera Connected Vision System with Multi View Analytics: A Comprehensive Surveycited by 1
6 more
  1. 2025CADE: Continual Weakly-supervised Video Anomaly Detection with Ensemblescited by 1
  2. 2026Are Multimodal LLMs Ready for Surveillance? A Reality Check on Zero-Shot Anomaly Detection in the Wildcited by 1
  3. 2026From Frames to Events: Rethinking Evaluation in Human-Centric Video Anomaly Detectioncited by 1
  4. 2026BLUE: Semantics-Preserving Video Compression for Efficient Vision-Language Surveillance Analyticscited by 0
  5. 2026VQ-VAD: Vector-quantized Motion Representation Learning for Human-centric Video Anomaly Detectioncited by 0
  6. 2026Identity-Centric Video Summarization via Hierarchical Fusion of Biometric, Appearance, and 3D Body Featurescited by 0