Detection of Event Using Trajectory Hyper-graphs Method

Abstract

The main function of machine vision is to improve the flexibility and automation of the production; extraction of semantic information in the video data is difficult in the field of machine vision. The video event detection involves the shooting of the surveillance video, and the detection and conversion to the data processing and analysis. This paper presents the trajectory hyper-graph theory, recognition of events in video and sub events, so it can strengthen the ability of event classification. By doing the experiments of several monitoring video (MV) datasets from different scenes for event detection, we find that the numbers of vertices and clusters of trajectory and multi-label hyper-graph fusion method (TG-MLG) are larger than these of the other two methods, and it has better description performance. Using the trajectory hyper-graph theory for detection of event, the high-level semantic information can be used for video classification, searching and forecasting.