A Hybrid Method for Human Interaction Recognition Using Spatio-temporal Interest Points

This paper proposes an innovative and effective hybrid way to recognize human interactions, which incorporates the advantages of both global feature (Motion Context, MC) and Spatio-Temporal (S-T) correlation of local Spatio-Temporal Interest Points (STIPs). The MC feature, which also derives from STIPs, is used to train a random forest where Genetic Algorithm (GA) is applied to the training phase to achieve a good compromise between reliability and efficiency. Besides, we design an effective and efficient S-T correlation based match to assist the MC feature, where MC's structure and a biological sequence matching algorithm are employed to calculate the spatial and temporal correlation score, respectively. Experiments on the UT-Interaction dataset show that our GA search based random forest and S-T correlation based match achieve better performance than some other prevalent machine leaning methods, and that a combination of those two methods outperforms most of the state-of-the-art works.