A Skeleton-based Approach for Campus Violence Detection

Omarov B. Narynov S. Zhumanov Z. Gumar A. Khassanova M.
2022 Tech Science Press

Computers, Materials and Continua
2022 #72 Issue 1 315 - 331 pp.

In this paper, we propose a skeleton-based method to identify violence and aggressive behavior. The approach does not necessitate high-processing equipment and it can be quickly implemented. Our approach consists of two phases: feature extraction from image sequences to assess a human posture, followed by activity classification applying a neural network to identify whether the frames include aggressive situations and violence. A video violence dataset of 400 min comprising a single person’s activities and 20 h of video data including physical violence and aggressive acts, and 13 classifications for distinguishing aggressor and victim behavior were generated. Finally, the proposed method was trained and tested using the collected dataset. The results indicate the accuracy of 97% was achieved in identifying aggressive conduct in video sequences. Furthermore, the obtained results show that the proposed method can detect aggressive behavior and violence in a short period of time and is accessible for real-world applications.

Artificial intelligence , Bullying , Machine learning , PoseNET , Skeleton , Violence

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Alem Research, Almaty, Kazakhstan
Al-Farabi Kazakh National University, Almaty, Kazakhstan
International University of Tourism and Hospitality, Turkistan, Kazakhstan
Suleiman Demirel University, Almaty, Kazakhstan
Asfendiyarov Kazakh National Medical University, Almaty, Kazakhstan

Alem Research
Al-Farabi Kazakh National University
International University of Tourism and Hospitality
Suleiman Demirel University
Asfendiyarov Kazakh National Medical University

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