On the Benefits of 3D Pose and Tracking for Human Action Recognition (2304.01199v2)

Abstract: 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

• The paper introduces a novel Lagrangian tracking method that leverages 3D pose data to improve action recognition.
• It integrates 3D pose with contextual appearance using a transformer fusion model, achieving superior mAP on the AVA dataset.
• The study demonstrates that robust spatiotemporal tracking can propel advances in surveillance, robotics, and human-computer interaction.

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

The paper "On the Benefits of 3D Pose and Tracking for Human Action Recognition" offers a detailed investigation into the application of 3D tracking and pose estimation for improving human action recognition in video data. By adopting the Lagrangian perspective, the authors present a novel method for recognizing actions by analyzing the trajectory of human motion across space-time rather than focusing on fixed spatial points.

Key Contributions and Methodology

The methodology central to this paper involves the use of person tracklets — sequences of frames where a person is consistently identified and followed over time. This approach leverages the inherent advantages of the Lagrangian perspective, which focuses on tracking individuals through spatiotemporal pathways rather than static observations.

1. 3D Pose and Person-Person Interactions: The authors initially demonstrate the effectiveness of 3D pose data in inferring human actions and understanding interactions between individuals. They employ a 3D tracking system, PHALP, along with the human mesh recovery model, HMR 2.0, to extract 3D representations of people within video frames.
2. Fusion Model for Action Recognition: Subsequent to the extraction of 3D data, the authors propose a model that integrates 3D pose data with contextualized appearance information derived from MaskFeat-pretrained Multiscale Vision Transformers (MViT). This fusion model processes these data types using a transformer network that predicts actions based on the gathered tracklets.
3. Empirical Evaluation and Results: The paper reports state-of-the-art performance on the AVA v2.2 dataset, with the "pose only" model achieving a mean average precision (mAP) gain of 10.0 over state-of-the-art equivalents. The fusion model further enhances performance, achieving a mAP of 45.1, reflecting a gain of 2.8 over previous benchmarks.

Implications and Future Directions

The research makes significant strides in advancing the efficacy of action recognition systems by incorporating and utilizing 3D spatial information. The notion that mere 2D pose estimation and appearance cues might be insufficient in certain scenarios is an important takeaway, suggesting avenues for further exploration into richer data representations.

1. Impact on Action Recognition Systems: This work underscores the significance of involving spatial-temporal tracking and 3D pose estimation in action detection frameworks, which could improve systems used in surveillance, human-computer interaction, and sports analytics.
2. Extending Beyond Current Work: The paper suggests that future work on action recognition should investigate more expressive models of human and object interaction. For instance, using more detailed body models like SMPL-X could capture hand movements and expressions, potentially leading to better performance in recognizing complex activities.
3. Transfer to Other Domains: The method's robust handling of dynamic environments might be applicable to other domains such as autonomous driving and robotics, where understanding human actions in real-time is crucial.

Conclusion

This paper provides compelling evidence that tracking and 3D pose estimation significantly enhance the accuracy of human action recognition systems. By implementing advancements in transformer models and leveraging comprehensive datasets for training, the authors open the door to new possibilities in video analysis technology. The paper serves as both a benchmark and a foundation for future developments, hinting at the broader applicability of 3D data in AI-driven interpretative tasks.