2D bidirectional gated recurrent unit convolutional Neural networks for end-to-end violence detection In videos
<|reference_start|>2D bidirectional gated recurrent unit convolutional Neural networks for end-to-end violence detection In videos: 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.<|reference_end|>
@article{traoré20242d,
    title={2D bidirectional gated recurrent unit convolutional Neural networks for
  end-to-end violence detection In videos},
    author={Abdarahmane Traor'e, Moulay A. Akhloufi},
    journal={2020 International Conference on Image Analysis and Recognition
  (ICIAR)},
    year={2024},
    doi={10.1007/978-3-030-50347-5_14},
    archivePrefix={arXiv},
    eprint={2409.07588},
    primaryClass={cs.CV}
}