Detecting Abnormal Behavior in Examination Surveillance Video with 3D Convolutional Neural Networks

Nowadays, people pay more attention to fairness of examination, so it is meaningful to detect abnormal behavior to ensure the order of examination. Most current methods propose models for particular cheating behavior. In this paper, we extract the optical flow of video data and propose a 3D convolution neural networks model to deal with the problem. The proposed model extracts the spatial and temporal features from video data and these features can be directly feed into the classifier for model learning or inference. The experiments on our own made dataset show that the proposed model achieves superior performance in comparison to current methods. The precision of the detection can reach 86.5%.