Abstract
In this study, we present a skeleton-based approach for detecting aggressive activity. The approach does not require much powerful hardware but is very fast in realization. There are two stages in our method: feature extraction from video frames to evaluate a person’s posture, and then action classification using a neural network to determine if the frames contain bullying scenes. We also selected 13 classes for identifying aggressor’s and victim’s behavior, created a dataset of 400 min of video data that contains actions of one person and 20 h of video data containing actions of physical bullying and aggression. The approach was tested on the assembled dataset. Results show more than 97% accuracy in determining aggressive behavior in video sequences.
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Narynov, S., Zhumanov, Z., Gumar, A., Khassanova, M., Omarov, B. (2021). Detecting School Violence Using Artificial Intelligence to Interpret Surveillance Video Sequences. In: Wojtkiewicz, K., Treur, J., Pimenidis, E., Maleszka, M. (eds) Advances in Computational Collective Intelligence. ICCCI 2021. Communications in Computer and Information Science, vol 1463. Springer, Cham. https://doi.org/10.1007/978-3-030-88113-9_32
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