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In this work we study the benefits of using tracking and 3D poses for actionrecognition. To achieve this, we take the Lagrangian view on analysing actionsover a trajectory of human motion rather than at a fixed point in space. Takingthis stand allows us to use the tracklets of people to predict their actions.In this spirit, first we show the benefits of using 3D pose to infer actions,and study person-person interactions. Subsequently, we propose a LagrangianAction Recognition model by fusing 3D pose and contextualized appearance overtracklets. To this end, our method achieves state-of-the-art performance on theAVA v2.2 dataset on both pose only settings and on standard benchmark settings.When reasoning about the action using only pose cues, our pose model achieves+10.0 mAP gain over the corresponding state-of-the-art while our fused modelhas a gain of +2.8 mAP over the best state-of-the-art model. Code and resultsare available at: https://brjathu.github.io/LART

On the Benefits of 3D Pose and Tracking for Human Action Recognition