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Hybrid Architecture for Real-Time Video Anomaly Detection: Integrating Spatial and Temporal Analysis

arXiv 2410.15909 ↗First posted 21 Oct 2024cs.CVcited by 0

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Abstract (arXiv)

In this paper, we propose a new architecture for real-time anomaly detection in video data, inspired by human behavior combining spatial and temporal analyses. This approach uses two distinct models: (i) for temporal analysis, a recurrent convolutional network (CNN + RNN) is employed, associating VGG19 and a GRU to process video sequences; (ii) regarding spatial analysis, it is performed using YOLOv7 to analyze individual images. These two analyses can be carried out either in parallel, with a final prediction that combines the results of both analysis, or in series, where the spatial analysis enriches the data before the temporal analysis. Some experimentations are been made to compare these two architectural configurations with each other, and evaluate the effectiveness of our hybrid approach in video anomaly detection.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 29 references that are in the map. First posted on arXiv 21 Oct 2024.

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16 of 29 references.

References · 16

  1. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  2. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
  3. 2015Fast R-CNNcited by 2,930
  4. 2018YOLOv3: An Incremental Improvementcited by 2,652
  5. 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,160
  6. 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,624
  7. 2016YOLO9000: Better, Faster, Strongercited by 1,429
  8. 2015Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcastingcited by 1,428
  9. 2022YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectorscited by 978
  10. 2019Object Detection in 20 Years: A Surveycited by 528
  11. 2015Delving Deeper into Convolutional Networks for Learning Video Representationscited by 146
  12. 2021You Only Learn One Representation: Unified Network for Multiple Taskscited by 86
4 more
  1. 2020Continual Learning for Anomaly Detection in Surveillance Videoscited by 49
  2. 2015Human Action Recognition using Factorized Spatio-Temporal Convolutional Networkscited by 40
  3. 2020Online Anomaly Detection in Surveillance Videos with Asymptotic Bounds on False Alarm Ratecited by 22
  4. 2020Video Anomaly Detection for Smart Surveillancecited by 16

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