Global anomaly detection based on a deep prediction neural network

Abstract

Abnormal event detection in public scenes is very important in recent society. In this paper, a method for global anomaly detection in video surveillance is proposed, which is based on a deep prediction neural network. The deep prediction neural network is built on the Convolutional Neural Network (CNN) and a variant of the Recurrent Neural Network (RNN)-Long Short-Term Memory (LSTM). Especially, the feature of a frame is the output of CNN, which is instead of the hand-crafted feature. First, the feature of a short video clip is obtained through CNN. Second, the predicted feature of the next frame can be gained by LSTM. Finally, the prediction error is introduced to detect that a frame is abnormal or not after the feature of the frame is achieved. Experimental results of global abnormal event detection show the effectiveness of our deep prediction neural network. Comparing with state-of-the-art methods, the model we proposed obtains superior detection results.