一种优化FCN的视频异常行为检测定位方法

在视频异常行为检测过程中,为了提取出可分辨性更好的特征,同时兼顾运行速度,提出一种基于优化的全卷积网络(full convolution network,FCN)的异常行为检测与定位方法.对FCN进行优化,使用卷积神经网络(convolution neural network,CNN)的数个初始卷积层和一个额外卷积层,...

A video abnormal behavior detection and location method of optimized FCN

In the process of video anomaly detection,in order to extract better distinguishable features and take into account the running speed, this paper proposes an anomaly detection and location method based on optimized full convolution network (FCN). Firstly, the FCN is optimized by using several initial convolution layers and an additional convolution layer of convolution neural network (CNN) to generate a set of regional vectors describing both motion and shape. Then, the set of eigenvectors is validated by using Gauss classifier, and the blocks with significant differences are marked as anomalies, and the suspicious regions with low fitting confidence are input to the sparse automatic encoder. Finally, the abnormal behavior is located and the location of the abnormal behavior is returned to FCN. The proposed method is validated and analyzed on two open data sets, UCSD and Subway. The experimental results show that the proposed method performs well in receiver operation characteristic (ROC) curve, equal error rate (EER) and area under curve (AUC). In addition, the proposed method has achieved a processing speed of 60 fps, indicating an excellent real time capability.