On the Benefits of 3D Pose and Tracking for Human Action Recognition
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Abstract (arXiv)
In this work we study the benefits of using tracking and 3D poses for action recognition. To achieve this, we take the Lagrangian view on analysing actions over a trajectory of human motion rather than at a fixed point in space. Taking this 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 Lagrangian Action Recognition model by fusing 3D pose and contextualized appearance over tracklets. To this end, our method achieves state-of-the-art performance on the AVA 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 model has a gain of +2.8 mAP over the best state-of-the-art model. Code and results are available at: https://brjathu.github.io/LART
Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). Builds on 54 of its 74 references that are in the map. First posted on arXiv 3 Apr 2023.
Builds on
54 of 74 references.
References · 54
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50 most cited of 54 All 54 (JSON)
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