Automated Human Violence Detection using MobileNetV2 and Bidirectional LSTM Networks
Year: 2024
Type: article
Abstract: A cutting-edge automated approach for identifying instances of human aggression in video footage is presented in this research study. By utilizing an advanced combination of MobileNetV2 and Bidirectional Long Short-Term Memory (LSTM) networks, the approach guarantees a strong analysis of consecutive frames. While Bidirectional LSTM records temporal relationships and allows for a more sophisticated understanding of activities over time, MobileNetV2 is a potent feature extractor. After being carefully trained on a wide range of datasets that include both violent and non-violent examples, the model obtains excellent performance metrics. The system has remarkable skill in properly identifying violent acts, with accuracy of 94.5%, precision of 93.0%, recall of 94.5%, and an outstanding F1 score of 95.9%. With its extensive modules for training, testing, data preparation, model design, and visualization, the project offers a solid foundation for critical assessment and real-world application. Subsequent research directions will concentrate on refining the architecture, investigating group learning techniques, and enhancing real-time inference, ultimately advancing automated violence detection in many real-world contexts. There is potential for revolutionary uses of this study in public safety, monitoring, and security.
Authors: Reddy, M. V. Lokeshwar, Vinit, S.M. Sai, Ankayarkanni, B., Sangeetha, P., Arun, P., Arulmurugan, T.
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6967743644
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