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Link to original content: https://unpaywall.org/10.1007/S00371-021-02266-4
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A multi-stream CNN for deep violence detection in video sequences using handcrafted features

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Abstract

Intelligent video surveillance systems have been used recently for automatic monitoring of human interactions. Although they play a significant role in reducing security concerns, there are many challenges for distinguishing between normal and abnormal behaviors such as crowded environments and camera viewpoint. In this paper, we propose a novel deep violence detection framework based on the specific features derived from handcrafted methods. These features are related to appearance, speed of movement, and representative image and fed to a convolutional neural network (CNN) as spatial, temporal, and spatiotemporal streams. The spatial stream trained the network with each frame in the video to learn environment patterns. The temporal stream contained three consecutive frames to learn motion patterns of violent behavior with a modified differential magnitude of optical flow. Moreover, in spatio-temporal stream, we introduced a discriminative feature with a novel differential motion energy image to represent violent actions more interpretable. This approach covers different aspects of violent behavior by fusing the results of these streams. The proposed CNN network is trained with violence-labeled and normal-labeled frames of 3 Hockey, Movie, and ViF datasets which comprised both crowded and uncrowded situations. The experimental results showed that the proposed deep violence detection approach outperformed state-of-the-art works in terms of accuracy and processing time.

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Authors and Affiliations

  1. School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran

    Seyed Mehdi Mohtavipour

  2. Iran Telecommunication Research Center, Tehran, Iran

    Mahmoud Saeidi & Abouzar Arabsorkhi

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  1. Seyed Mehdi Mohtavipour
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Mohtavipour, S.M., Saeidi, M. & Arabsorkhi, A. A multi-stream CNN for deep violence detection in video sequences using handcrafted features. Vis Comput 38, 2057–2072 (2022). https://doi.org/10.1007/s00371-021-02266-4

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