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2D Bidirectional Gated Recurrent Unit Convolutional Neural Networks for End-to-End Violence Detection in Videos

Part of the Lecture Notes in Computer Science book series (LNIP,volume 12131)

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.

Keywords

  • CNN
  • GRU
  • Abnormal behavior detection
  • Violence detection
  • Video classification

Thanks to the Natural Sciences and Engineering Research Council of Canada (NSERC), [funding reference number RGPIN-2018-06233].

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Correspondence to Moulay A. Akhloufi .

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Traoré, A., Akhloufi, M.A. (2020). 2D Bidirectional Gated Recurrent Unit Convolutional Neural Networks for End-to-End Violence Detection in Videos. In: Campilho, A., Karray, F., Wang, Z. (eds) Image Analysis and Recognition. ICIAR 2020. Lecture Notes in Computer Science(), vol 12131. Springer, Cham. https://doi.org/10.1007/978-3-030-50347-5_14

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  • DOI: https://doi.org/10.1007/978-3-030-50347-5_14

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