With the rapid development of the applications of network

enormous multimedia files are emerging every day. Video data is an integration of text

sound

image and other files. Not only being hierarchical

structural and complex

video data is also rich in semantic information. Therefore

extensive attentions have been drawn to how to process video data quickly

extract the video characteristics accurately

and analyze and understand the semantic content deeply. Semantic event detection and analysis could find video information quickly and accurately for users in the vast ocean of video data. It can be applied to the field of video on demand

intelligent monitoring and video mining as well. However

there are still many limitations

such as low recognition rate for multiple moving objects with different characteristics

low accuracy in semantic event detection

difficulties in detecting semantic event correlations

the lack of consistent standards of event semantic description

and so on. The detection and analysis methods of complex event based on matching integration between trajectory and multi-label hypergraphs are proposed. Trajectory and multi-label hypergraphs are constructed for classifying and recognizing the complex events. By matching the trajectory hypergraph and multi-label hypergraph

mapping relationship between trajectory and multiple semantic labels is built to extract the complex semantic events. The recognition of low-level features to high-level semantic is made possible for video events. Compared with other methods

such as event detection method based on graph and multi-label semi-supervised learning methods based on hypergraph

the proposed method has a higher mean average recall rate and mean average accuracy rate in the detection result of the complex event. we propose a new event detection method that is trajectory and multi-label hypergraphs model in this paper. This model is a widely-used detection and analysis of complex semantic-based events method. In their clustering process

their number of vertices and clusters has been more than other graph methods. But

the event detection result is better than others.