Trajectory Classification and Recognition of Moving Objects
Pan Qi-ming · Fire Control and Command Control · 2009
Trajectory recognition of the moving objects is the basic problem of the movement analysis. The intentions are as follows:interpreting what has happened in surveillance scenes,analyzing and recognizing the trajectory activity patterns of the objects in real scenes,classifying them automatically and intelligently. After judging the validity of the trajectories,use the K-Means to cluster them. Using modified Hidden Markov Model,firstly,aiming at the complex degree of the trajectories,the models are built for every trajectory pattern,and the training samples are used to get the credible parameters of the model,finally,the maximum likelihood probability of the test samples are computed to all of the trained models,the maximum value is saved and the corresponding model is the recognition result. Then train and recognize the samples clustered,the average recognition rate is high,and the method is efficient.