An Efficient CNN and BI-LSTM Model for Abnormal Event Detection in Video Surveillance

Abstract

Abnormal event detection plays a complex and important task in intelligent transportation and Computer vision. At present, the existing abnormal event detection models based on deep learning mainly focus on data represented by a vectorial form, which pay little attention to the impact of the internal structure characteristics of feature vector. In order to address the above issues, we propose an abnormal event detection hybrid modulation method via feature extraction calibrating classification in video surveillance scenes in this paper. Our main contribution is to calibrate the classification of an abnormal event by constructing feature expectation subgraphs. First, we employ convolutional neural network and long short-term memory models to extract the spatiotemporal features of video frame, and then construct the feature expectation subgraph for each key frame of every video, which could be used to capture the internal sequential and topological relational characteristics of structured feature vector. This model included a pre-trained CNN for extracting spatio-temporal features from each individual frame selected from a series of frames, which is then passed to multi-layer Bi-directional Long Short-term Memory (BD-LSTM) that possesses the capability of accurately classifying the abnormal events in complex surveillance scenes of the road. Finally, the experiments and performance of frame level and pixel level values on a common UMN dataset are estimated and evaluated.