## 3 Background  
### Upper Bounds of Analytical Framework  
In this section, we will discuss the upper-bound performance of the analytical system, with a specific focus on the multi-object tracking accuracy (MOTA) metric. Our re-implementation of the validation set achieves a slightly higher performance (MOTA: 57.8) compared to the original paper's reported performance (MOTA: 57.6). We will explore the upper bound performance of the analytical framework by considering three key factors: Track Identifier (track ID), detection bounding boxes, and body joint landmarks. We analyze these factors from four different aspects:  
* Perfect detection bounding boxes: We utilize the ground truth bounding boxes as the predicted results of the detector. Subsequently, we estimate the body joint landmarks within these ground truth boxes. The predicted landmarks are then matched using a bounding box overlap cost criterion. As illustrated in Table 1, we observe a notable 5.1% improvement in MOTA (57.8 \(\rightarrow\) 62.9). The lack of fusion of potential information from adjacent frames indicates that the performance of the image-level detector is insufficient under the circumstances of frame degeneration, and its influence cannot be overlooked.
* Perfect body joint landmarks: In this scenario, we first employ the detector to predict a series of bounding boxes. We then calculate the Intersection-over-Union (IoU) between the predicted boxes and the ground truth boxes. We assign ground truth keypoints to the predicted boxes with an IoU greater than 0.7. As depicted in Table 1, we achieve a  
Figure 2: Our spatiotemporal sampling framework consists of three primary stages. **(1)** Person Detection Stage: This stage focuses on generating bounding boxes, which are subsequently utilized to predict body joint landmarks in the human pose estimation stage. **(2)** Human Pose Estimation Stage: Building upon the detection boxes generated in the first stage, this stage predicts keypoints associated with human poses. **(3)** Multi-Person Articulated Tracking Stage: In this stage, a lightweight tracking algorithm based on greedy matching is employed to address the more intricate task of tracking poses. It utilizes the detection boxes from the first stage and the keypoints predicted in the second stage to track poses over time.  
significant 10.8% improvement in MOTA (57.8 \(\rightarrow\) 68.6). This highlights the crucial role played by the quality of pose estimation in the evaluation of tracking performance.
* Perfect track ID: Similar to the approach described in (2), we obtain boxes (IoU \(>\) 0.7) and assign the ground truth track IDs to the final boxes. As outlined in Table 1, we observe a modest 1.8% improvement in MOTA (57.8 \(\rightarrow\) 59.6). This suggests that a simple and commonly used greedy matching algorithm, based on the bounding box overlap cost criterion, is already approaching the upper bound performance.
* Perfect bounding boxes and body joint landmarks: Finally, we combine the perfect bounding boxes and body joint landmarks. As indicated in Table 1, this results in a substantial boost in MOTA (57.8 \(\rightarrow\) 74.9). This emphasizes that the most critical challenge in the articulated pose tracking task for the top-down pipeline lies in establishing a robust and reliable person detector and pose estimator.