结合卷积与循环网络，用光流检测视频暴力行为
Violence detection in videos using deep recurrent and convolutional neural networks
- 用2D CNN提取帧内空间特征，RNN捕捉时间序列变化
- 融合光流信息后，在3个数据集上达到顶尖水平
- 适合需要实时动作分析的安防系统应用
近年来，由于全球大城市犯罪率上升，暴力及异常行为检测研究受到越来越多关注。本文提出一种深度学习架构，结合二维卷积神经网络（2D CNN）与循环神经网络（RNN），用于视频中暴力行为检测。除了原始视频帧外，还利用捕获序列计算出的光流信息，以编码场景中的运动特征。其中，CNN负责提取每帧的空间特性，RNN则捕捉时间上的动态变化。实验在三个公开数据集上验证，所提方法性能达到甚至超过当前最先进水平。
原文摘要 · Abstract (English)
Violence and abnormal behavior detection research have known an increase of interest in recent years, due mainly to a rise in crimes in large cities worldwide. In this work, we propose a deep learning architecture for violence detection which combines both recurrent neural networks (RNNs) and 2-dimensional convolutional neural networks (2D CNN). In addition to video frames, we use optical flow computed using the captured sequences. CNN extracts spatial characteristics in each frame, while RNN extracts temporal characteristics. The use of optical flow allows to encode the movements in the scenes. The proposed approaches reach the same level as the state-of-the-art techniques and sometime surpass them. It was validated on 3 databases achieving good results.
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