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

• URL: http://arxiv.org/abs/2409.07588v1
• Date: Wed, 11 Sep 2024 19:36:12 GMT
• Title: 2D bidirectional gated recurrent unit convolutional Neural networks for end-to-end violence detection In videos
• Authors: Abdarahmane Traoré, Moulay A. Akhloufi,
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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