A unified framework for locating and recognizing human actions

In this paper, we present a pose based approach for locating and recognizing human actions in videos. In our method, human poses are detected and represented based on deformable part model. To our knowledge, this is the first work on exploring the effectiveness of deformable part models in combining human detection and pose estimation into action recognition. Comparing with previous methods, ours have three main advantages. First, our method does not rely on any assumption on video preprocessing quality, such as satisfactory foreground segmentation or reliable tracking; Second, we propose a novel compact representation for human pose which works together with human detection and can well represent the spatial and temporal structures inside an action; Third, with human detection taken into consideration in our framework, our method has the ability to locate and recognize multiple actions in the same scene. Experiments on benchmark datasets and recorded cluttered videos verified the efficacy of our method.