Hierarchical Hough forests for view-independent action recognition

Appearance-based action recognition can be considered as a natural extension of appearance-based object detection from the spatial to the spatio-temporal domain. Although this step seems natural, most action recognition approaches are evaluated in isolation. Towards this end the contribution of this paper is twofold. First, a view-independent approach to action recognition is proposed and second the sensitivity w.r.t. a combination of person detection and action recognition is evaluated. Action recognition is performed in a hierarchical manner: First, the relative camera orientation in the scene is estimated and second, the action is determined using view-dependent Hough forests. The proposed approach is evaluated on the multi-view i3DPost dataset [1] and its performance is compared to single-step approaches using Hough forests. The results suggest that the recognition rate increases, when using the proposed hierarchical method compared to single-step approaches. Further, the performance rates of hierarchical Hough forests on ground truth data are compared to the results of hierarchical Hough forests in combination with a person detector.