Detecting Global Exam Events in Invigilation Videos Using 3D Convolutional Neural Network

摘要

This paper designs a structure of 3D convolutional neural network to detect the global exam events in invigilation videos. Exam events in invigilation videos are defined according to the human activity performed at a certain phase in the entire exam process. Unlike general event detection which involves different scenes, global event detection focuses on differentiating different collective activities in the exam room ambiance. The challenges lie in the great intra-class variations within the same type of events due to various camera angles and different exam room ambiances, as well as inter-class similarities which are challengeable. This paper adopts the 3D convolutional neural network based on its ability in extracting spatio-temporal features and its effectiveness in detecting video events. Experiment results show the designed 3D convolutional neural network achieves an accuracy of its capability of 93.94% in detecting the global exam events, which demonstrates the effectiveness of our model.