用2D CNN与BiGRU结合，端到端检测视频中的暴力行为
2D bidirectional gated recurrent unit convolutional Neural networks for end-to-end violence detection In videos
- 融合2D CNN提取帧内空间特征，BiGRU捕捉多帧时序运动特征
- 在三个公开数据集上达到最高98%的准确率，表现优异
- 适合需要实时暴力识别的安防场景应用
异常行为检测、动作识别及视频中打斗与暴力事件检测近年来备受关注。本文提出一种结合双向门控循环单元（BiGRU）与二维卷积神经网络（2D CNN）的架构，用于检测视频序列中的暴力行为。2D CNN负责从每帧中提取空间特征，而BiGRU则利用多帧的CNN特征提取时间与局部运动特征。所提出的端到端深度学习模型在三个具有不同场景复杂度的公开数据集上进行了测试，最高准确率达到98%。结果表明该方法性能良好，验证了端到端方案的有效性。
原文摘要 · Abstract (English)
Abnormal behavior detection, action recognition, fight and violence detection in videos is an area that has attracted a lot of interest in recent years. In this work, we propose an architecture that combines a Bidirectional Gated Recurrent Unit (BiGRU) and a 2D Convolutional Neural Network (CNN) to detect violence in video sequences. A CNN is used to extract spatial characteristics from each frame, while the BiGRU extracts temporal and local motion characteristics using CNN extracted features from multiple frames. The proposed end-to-end deep learning network is tested in three public datasets with varying scene complexities. The proposed network achieves accuracies up to 98%. The obtained results are promising and show the performance of the proposed end-to-end approach.
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