基于拉格朗日场的多级运动特征暴力行为识别

针对暴力行为识别过程中缺乏描述不同时间尺度下暴力行为运动变化的问题，本文提出了一种基于拉格朗日场的多级运动特征暴力行为识别算法。该算法将描述非线性粒子运动的拉格朗日场引入暴力行为分析过程中，首先通过构建基于光流的拉格朗日场来挖掘不同时间尺度下暴力行为运动特征，设计了基于拉格朗日场的多级运动模块，该模块可以根据输入光流序列长度，计算多级运动特征；然后构建了基于流量门控制机制的双流网络，将多级运动特征和RGB图像特征融合；最后，利用LSTM和全连接模型计算识别结果。实验证明，该方法在公共暴力识别数据集上取得了很好的效果，特别是在真实监控场景的RWF-2000数据集上，暴力行为识别正确识别率可以达到88.4%，优于其他算法。 

Violence Recognition Based on Multilevel-motion Features of Lagrange Field

? ?In different time scales in the process of violence recognition， a multilevel-motion feature violence recognition algorithm based on Lagrange field is proposed in this paper. In this algorithm， the Lagrange field describing the nonlinear particle motion is introduced into the process of violence analysis. The opt Lagrange field based on optical flow is constructed to mine the motion characteristics of violence at different time scales， and a multilevel-motion module based on Lagrange field is designed. The module can calculate the multilevel-motion characteristics according to the length of the input optical flow sequence； Then， a dual flow network based on flow gate control mechanism is constructed to fuse multilevel-motion features and RGB image features； Finally， the recognition results are calculated by using LSTM and full connection model. Experiments show that this method has achieved good results in public violence identification data set， especially in RWF-2000 data set of real monitoring scene， the correct recognition rate of violence identification can reach 88.4%， which is better than other algorithms.