mlsanityData to 14 Oct 2026

Clustering Aided Weakly Supervised Training to Detect Anomalous Events in Surveillance Videos

arXiv 2203.13704 ↗First posted 25 Mar 2022cs.CVcited by 12

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Abstract (arXiv)

Formulating learning systems for the detection of real-world anomalous events using only video-level labels is a challenging task mainly due to the presence of noisy labels as well as the rare occurrence of anomalous events in the training data. We propose a weakly supervised anomaly detection system which has multiple contributions including a random batch selection mechanism to reduce inter-batch correlation and a normalcy suppression block which learns to minimize anomaly scores over normal regions of a video by utilizing the overall information available in a training batch. In addition, a clustering loss block is proposed to mitigate the label noise and to improve the representation learning for the anomalous and normal regions. This block encourages the backbone network to produce two distinct feature clusters representing normal and anomalous events. Extensive analysis of the proposed approach is provided using three popular anomaly detection datasets including UCF-Crime, ShanghaiTech, and UCSD Ped2. The experiments demonstrate a superior anomaly detection capability of our approach.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 53 of its 116 references that are in the map. First posted on arXiv 25 Mar 2022.

Builds on

53 of 116 references.

References · 53

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38 more
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50 most cited of 53 All 53 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 1; 2024: 6; 2025: 3; 2026: 1.

12 papers.

  1. 2024Deep Learning for Video Anomaly Detection: A Reviewcited by 27
  2. 2024Collaborative Learning of Anomalies with Privacy (CLAP) for Unsupervised Video Anomaly Detection: A New Baselinecited by 17
  3. 2025Anomaly Detection and Generation with Diffusion Models: A Surveycited by 8
  4. 2024Exploiting Autoencoder's Weakness to Generate Pseudo Anomaliescited by 4
  5. 2025The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLMcited by 4
  6. 2025Learning Event Completeness for Weakly Supervised Video Anomaly Detectioncited by 3
  7. 2026Weakly Supervised Video Anomaly Detection with Anomaly-Connected Components and Intention Reasoningcited by 3
  8. 2024Multimodal Attention-Enhanced Feature Fusion-based Weekly Supervised Anomaly Violence Detectioncited by 2
  9. 2023A Lightweight Video Anomaly Detection Model with Weak Supervision and Adaptive Instance Selectioncited by 1
  10. 2024Constricting Normal Latent Space for Anomaly Detection with Normal-only Training Datacited by 1
  11. 2022Real-world Video Anomaly Detection by Extracting Salient Features in Videoscited by 0
  12. 2024Joint Stream: Malignant Region Learning for Breast Cancer Diagnosiscited by 0