## 2 Related Work
### Multi-Person Pose Estimation and Tracking in Videos
#### 2.2.2 Multi-Person Pose Tracking  
In the context of multi-person pose tracking in videos, there are two main approaches: bottom-up and top-down.  
* Bottom-Up Approaches: These approaches start by detecting human body parts and then group them to form individuals. The grouped parts are connected and associated across frames to generate complete poses. However, bottom-up approaches may struggle with body part association in occluded scenes. In [4; 5], the multi-person pose tracking challenge is introduced, and a spatial graph is extended to a spatiotemporal graph based on bottom-up methods [29]. While [4] achieves plausible results in complex videos by solving a minimum-cost multicut problem, the handcrafted features in probabilistic graphical models are not necessarily optimal for long video clips. Optimizing this sophisticated integer programming problem is a Nondeterministic Polynomial-time hard problem considered computationally infeasible, even with state-of-the-art optimizers.
* Top-Down Approaches: Similar to image-based top-down approaches, these methods face the challenge of reliably detecting individual persons, especially under significant occlusion [7; 26]. To address missed occluded joints, ref. [30] propose a Graph Neural Network (GNN)-based network that predicts poses aggregated with the detected poses. The process of posture prediction learning captures two types of human motion features through different types of edges: the relative motion and spatial structure of human joints within the frame, and the temporal human dynamic features between consecutive frames. Additionally, ref. [18] propose a gated attention transformer for multi-person pose tracking, considering posture-based similarity and appearance-based similarity. This approach automatically adjusts the impact of the two similarities on pose tracking accuracy and shows significant improvements in motion blur, crowded scenes, and occlusions, especially in assigning highly occluded persons in unusual poses. These algorithms and models primarily focus on improving the accuracy of pose tracking without addressing the issue of lightweight deployment. Specifically, this approach facilitates seamless model deployment and achieves commendable performance in human keypoint detection and tracking, all while maintaining low algorithmic complexity.  
Our articulated pose-tracking framework follows a multi-stage pipeline and offers advantages over object detection techniques used in videos. We propose an end-to-end trainable network that jointly estimates optical flow or samples features from adjacent frames. The predicted optical flow aligns the features, and a spatiotemporal module learns to spatially sample features from adjacent frames. The aggregated features are then fed into different detection networks to estimate body joint landmarks. By effectively fusing information from multiple frames, this approach naturally makes our framework robust to motion blur and the presence of multiple overlapping instances in individual frames. Moreover, our approach is much less computationally expensive compared to previous methods, allowing it to handle any number of person instances per frame in a video efficiently. Figure 2 depicts our spatiotemporal sampling framework, which we elaborate on in the subsequent sections.