Two-Stage Video Violence Detection Framework Using GMFlow and CBAM-Enhanced ResNet3D

Mohamed Mahmoud, Bilel Yagoub, Mostafa Farouk Senussi, Mahmoud Abdalla, Mahmoud Salaheldin Kasem and Hyun-Soo Kang ()
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Mohamed Mahmoud: Department of Information and Communication Engineering, School of Electrical and Computer Engineering, Chungbuk National University, Cheongju-si 28644, Republic of Korea
Bilel Yagoub: Department of Information and Communication Engineering, School of Electrical and Computer Engineering, Chungbuk National University, Cheongju-si 28644, Republic of Korea
Mostafa Farouk Senussi: Department of Information and Communication Engineering, School of Electrical and Computer Engineering, Chungbuk National University, Cheongju-si 28644, Republic of Korea
Mahmoud Abdalla: Department of Information and Communication Engineering, School of Electrical and Computer Engineering, Chungbuk National University, Cheongju-si 28644, Republic of Korea
Mahmoud Salaheldin Kasem: Department of Information and Communication Engineering, School of Electrical and Computer Engineering, Chungbuk National University, Cheongju-si 28644, Republic of Korea
Hyun-Soo Kang: Department of Information and Communication Engineering, School of Electrical and Computer Engineering, Chungbuk National University, Cheongju-si 28644, Republic of Korea

Mathematics, 2025, vol. 13, issue 8, 1-20

Abstract: Video violence detection has gained significant attention in recent years due to its applications in surveillance and security. This paper proposes a two-stage framework for detecting violent actions in video sequences. The first stage leverages GMFlow, a pre-trained optical flow network, to capture the temporal motion between consecutive frames, effectively encoding motion dynamics. In the second stage, we integrate these optical flow images with RGB frames and feed them into a CBAM-enhanced ResNet3D network to capture complementary spatiotemporal features. The attention mechanism provided by CBAM enables the network to focus on the most relevant regions in the frames, improving the detection of violent actions. We evaluate the proposed framework on three widely used datasets: Hockey Fight, Crowd Violence, and UBI-Fight. Our experimental results demonstrate superior performance compared to several state-of-the-art methods, achieving AUC scores of 0.963 on UBI-Fight and accuracies of 97.5% and 94.0% on Hockey Fight and Crowd Violence, respectively. The proposed approach effectively combines GMFlow-generated optical flow with deep 3D convolutional networks, providing robust and efficient detection of violence in videos.

Keywords: video violence detection; GMFlow; optical flow; CBAM (convolutional block attention module); ResNet3D; anomaly detection (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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