Features of Interest Points Based Human Interaction Prediction

The recognition and prediction of human interaction based on video has a broad application prospect in intelligent video monitoring and other fields, but the current integration algorithm of it is not mature, which greatly limits the application of the algorithm. A prediction method of human interaction based on the statistical characteristics of interest points is proposed, which established an integrated framework to the recognition and prediction of human interaction initially. First, the spatio-temporal interest points are extracted and performed as 3D-SIFT feature description, then the bag of words is used to represent the action video. In the training stage, Gaussian models are used to establish the action model for each action at different time scales. In the prediction stage, the bag of words representation is extracted and compared with the established prediction models of different time lengths to obtain similar prediction probabilities between the models for an action video of unknown length. Finally, the predictive probability of each action at different time scales is fused by weighted probabilities completing the recognition and prediction of interaction prediction. The experimental result on UT-interaction dataset demonstrated that the proposed approach is easy to implement and has a good predictive effect.