用注意力机制提升视频冲突检测准确率
Application of Attention Mechanism with Bidirectional Long Short-Term Memory (BiLSTM) and CNN for Human Conflict Detection using Computer Vision
- 融合CNN、BiLSTM与注意力机制捕捉时空特征
- 模型在复杂交互中实现更高检测精度
- 适合智能监控与公共安全系统开发者参考
通过视频自动检测人类冲突是计算机视觉的重要方向，广泛应用于监控与公共安全。然而，公开数据集稀缺及人类互动的复杂性使该任务极具挑战。本研究结合注意力机制、卷积神经网络（CNN）与双向长短期记忆网络（BiLSTM），提升视频中暴力行为的检测性能。实验表明，该组合能有效聚焦视频关键区域，增强模型准确性与鲁棒性。结果为实时暴力事件监测系统提供了可行方案，推动自动化安防技术发展。
原文摘要 · Abstract (English)
The automatic detection of human conflicts through videos is a crucial area in computer vision, with significant applications in monitoring and public safety policies. However, the scarcity of public datasets and the complexity of human interactions make this task challenging. This study investigates the integration of advanced deep learning techniques, including Attention Mechanism, Convolutional Neural Networks (CNNs), and Bidirectional Long ShortTerm Memory (BiLSTM), to improve the detection of violent behaviors in videos. The research explores how the use of the attention mechanism can help focus on the most relevant parts of the video, enhancing the accuracy and robustness of the model. The experiments indicate that the combination of CNNs with BiLSTM and the attention mechanism provides a promising solution for conflict monitoring, offering insights into the effectiveness of different strategies. This work opens new possibilities for the development of automated surveillance systems that can operate more efficiently in real-time detection of violent events.
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