Video Event Detection Method Research Based On The Hidden Markov Model

With the continuous development of the times,the multimedia information is everywhere. The information such as video, audio, images, compared with the plain text information, become the main source of information received for people. Video has more complex structure, richer semantic information than the rest of the information media. Its semantic research is gradually concerned by people. It has a strong theoretical and practical significance.Video is a series of time ordered picture frames.According to this, you can use a stochastic process model to analyze the process of transfer of video frames. Hidden Markov model is an effective method for this. Making video event detection, the first is sub-shot segmentation, we need to extract the underlying characteristics of each sub-lens.Get the sub-shots comprehensive characteristics by dimension reduction, dimensions unification. Then, we cluster all the training video sub-lens characteristic to get typical fragment and its cluster center. Next, feature compression will be implemented, reducing the number of sub-shots, to get the final video sub-shot feature data. And regard the sub_lens characteristic of the video as sequences of observations, video events related typical fragment as hidden state, to set up hidden markov model for each event. Use the feature data of training video to train the unknown parameters of the model.We train an HMM for each video event. Finally, the sub-shot feature data of a test video as observation sequence, matching the existing HMM, find the event when test video observation sequence occurs highest probability matching the known model, the event as the result of the test video. The experimental results verify the effectiveness of video event detection based on Hidden Markov Model.