Recognizing human interactions by genetic algorithm-based random forest spatio-temporal correlation.

Recognizing human interactions is a more challenging task than recognizing single person activities and has attracted much attention of the computer vision community. This paper proposes an innovative and effective way to recognize human interactions, which incorporates the advantages of both global motion context (MC) feature and spatio-temporal (S-T) correlation of local spatio-temporal interest point feature. The MC feature 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 propose S-T correlation-based match, where MC's structure and Needleman-Wunsch algorithm are used to calculate the spatial and temporal correlation score of two videos, respectively. Experiments on the UT-Interaction dataset show that our approaches outperform other prevalent machine learning methods, and that the combination of GA search-based random forest and S-T correlation achieves the state-of-the-art performance. [ABSTRACT FROM AUTHOR]