Localizing volumetric motion for action recognition in realistic videos

This paper presents a novel motion localization approach for recognizing actions and events in real videos. Exam-ples include StandUp and Kiss in Hollywood movies. The challenge can be attributed to the large visual and motion variations imposed by realistic action poses. Previous works mainly focus on learning from descriptors of cuboids around space time interest points (STIP) to characterize actions. The size, shape and space-time position of cuboids are fixed without considering the underlying motion dynamics. This often results in large set of fragmentized cuboids which fail to capture long-term dynamic properties of realistic actions. This paper proposes the detection of spatio-temporal mo-tion volumes (namely Volume of Interest, VOI) of scale and position adaptive to localize actions. First, motions are de-scribed as bags of point trajectories by tracking keypoints along the time dimension. VOIs are then adaptively ex-tracted by clustering trajectory on the motion mainfold. The resulting VOIs, of varying scales and centering at arbi-trary positions depending on motion dynamics, are eventu-ally described by SIFT and 3D gradient features for action recognition. Comparing with fixed-size cuboids, VOI allows comprehensive modeling of long-term motion and shows bet-ter capability in capturing contextual information associated with motion dynamics. Experiments on a realistic Holly-wood movie dataset show that the proposed approach can achieve 20 % relative improvement compared to the state-of-the-art STIP based algorithm