2D bidirectional gated recurrent unit convolutional Neural networks for end-to-end violence detection In videos

Abdarahmane Traoré, Moulay A. Akhloufi
9/11/2024
cs.CV

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Abstract

Abnormal behavior detection, action recognition, fight and violence detection in videos is an area that has attracted a lot of interest in recent years. In this work, we propose an architecture that combines a Bidirectional Gated Recurrent Unit (BiGRU) and a 2D Convolutional Neural Network (CNN) to detect violence in video sequences. A CNN is used to extract spatial characteristics from each frame, while the BiGRU extracts temporal and local motion characteristics using CNN extracted features from multiple frames. The proposed end-to-end deep learning network is tested in three public datasets with varying scene complexities. The proposed network achieves accuracies up to 98%. The obtained results are promising and show the performance of the proposed end-to-end approach.

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Cite this paper

@article{traor2024bidirectional,
  title  = {2D bidirectional gated recurrent unit convolutional Neural networks for end-to-end violence detection In videos},
  author = {Abdarahmane Traoré and Moulay A. Akhloufi},
  year   = {2024},
  eprint = {2409.07588},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2409.07588v1}
}

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