Action recognition with motion-appearance vocabulary forest

In this paper we propose an approach for action recognition based on a vocabulary forest of local motion-appearance features. Large numbers of features with associated motion vectors are extracted from action data and are represented by many vocabulary trees. Features from a query sequence are matched to the trees and vote for action categories and their locations. Large number of trees make the process efficient and robust. The system is capable of simultaneous categorization and localization of actions using only a few frames per sequence. The approach obtains excellent performance on standard action recognition sequences. We perform large scale experiments on 17 challenging real action categories from Olympic Games 1 . We demonstrate the robustness of our method to appearance variations, camera motion, scale change, asymmetric actions, background clutter and occlusion.