mlsanityData to 14 Oct 2026

TeG: Temporal-Granularity Method for Anomaly Detection with Attention in Smart City Surveillance

arXiv 2411.11003 ↗First posted 17 Nov 2024cs.CVcited by 1

Not on the map. Why?

Abstract (arXiv)

Anomaly detection in video surveillance has recently gained interest from the research community. Temporal duration of anomalies vary within video streams, leading to complications in learning the temporal dynamics of specific events. This paper presents a temporal-granularity method for an anomaly detection model (TeG) in real-world surveillance, combining spatio-temporal features at different time-scales. The TeG model employs multi-head cross-attention blocks and multi-head self-attention blocks for this purpose. Additionally, we extend the UCF-Crime dataset with new anomaly types relevant to Smart City research project. The TeG model is deployed and validated in a city surveillance system, achieving successful real-time results in industrial settings.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 24 of its 39 references that are in the map. First posted on arXiv 17 Nov 2024.

Builds on

24 of 39 references.

References · 24

  1. 2017Attention Is All You Needcited by 60,361
  2. 2021Video Swin Transformercited by 1,091
  3. 2018Real-world Anomaly Detection in Surveillance Videoscited by 577
  4. 2018GANomaly: Semi-Supervised Anomaly Detection via Adversarial Trainingcited by 409
  5. 2020Not only Look, but also Listen: Learning Multimodal Violence Detection under Weak Supervisioncited by 238
  6. 2021Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learningcited by 230
  7. 2016Multiple Instance Learning: A Survey of Problem Characteristics and Applicationscited by 164
  8. 2020Anomaly Detection in Video via Self-Supervised and Multi-Task Learningcited by 148
  9. 2022Multiview Transformers for Video Recognitioncited by 148
  10. 2021A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Predictioncited by 129
  11. 2022Generative Cooperative Learning for Unsupervised Video Anomaly Detectioncited by 102
  12. 2022MGFN: Magnitude-Contrastive Glance-and-Focus Network for Weakly-Supervised Video Anomaly Detectioncited by 89
12 more
  1. 2019GODS: Generalized One-class Discriminative Subspaces for Anomaly Detectioncited by 64
  2. 2020Localizing Anomalies from Weakly-Labeled Videoscited by 52
  3. 2019Motion-Aware Feature for Improved Video Anomaly Detectioncited by 48
  4. 2021Weakly-Supervised Spatio-Temporal Anomaly Detection in Surveillance Videocited by 32
  5. 2021Convolutional Transformer based Dual Discriminator Generative Adversarial Networks for Video Anomaly Detectioncited by 31
  6. 2021Semi-orthogonal Embedding for Efficient Unsupervised Anomaly Segmentationcited by 26
  7. 2022Bayesian Nonparametric Submodular Video Partition for Robust Anomaly Detectioncited by 23
  8. 2023BatchNorm-based Weakly Supervised Video Anomaly Detectioncited by 22
  9. 2022Multi-Contextual Predictions with Vision Transformer for Video Anomaly Detectioncited by 10
  10. 2022Adaptive Graph Convolutional Networks for Weakly Supervised Anomaly Detection in Videoscited by 7
  11. 2022Anomaly detection in surveillance videos using transformer based attention modelcited by 4
  12. 2024Detection of Object Throwing Behavior in Surveillance Videoscited by 2

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025M2S2L: Mamba-based Multi-Scale Spatial-temporal Learning for Video Anomaly Detectioncited by 1