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<p>Video violence detection poses significant challenges due to the complex integration of spatial, temporal, and contextual features. Conventional methods, such as 3D Convolutional Neural Networks, Long Short-Term Memory Models, and You Only Look Once, exhibit limited scene-level semantic understanding, high computational costs, and poor generalization across diverse environments. This work proposes a novel hybrid context-aware framework that combines a lightweight MobileNetV2 with a Bidirectional Long Short-Term Memory (BiLSTM) network for efficient spatiotemporal feature extraction. YOLOv8 is used for real-time object detection and Bootstrapping Language-Image Pre-training is utilized for generating the natural language captions to enhance high-level semantic understanding of scenes. The violence detection module is trained on the Real-Life Violence Situations (RLVS) dataset, achieving classification accuracy of 92.49%.</p>

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A hybrid context-aware video violence detection framework using hierarchical spatiotemporal and semantic modeling

  • 期刊论文

  • 发表日期：

    2026年1月14日

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  • Shobha Kumari,
  • Vijay Kumar

摘要

Video violence detection poses significant challenges due to the complex integration of spatial, temporal, and contextual features. Conventional methods, such as 3D Convolutional Neural Networks, Long Short-Term Memory Models, and You Only Look Once, exhibit limited scene-level semantic understanding, high computational costs, and poor generalization across diverse environments. This work proposes a novel hybrid context-aware framework that combines a lightweight MobileNetV2 with a Bidirectional Long Short-Term Memory (BiLSTM) network for efficient spatiotemporal feature extraction. YOLOv8 is used for real-time object detection and Bootstrapping Language-Image Pre-training is utilized for generating the natural language captions to enhance high-level semantic understanding of scenes. The violence detection module is trained on the Real-Life Violence Situations (RLVS) dataset, achieving classification accuracy of 92.49%.