## 1 Introduction  
Pose estimation and tracking, being a fundamental domain in computer vision, have significantly contributed to various applications dedicated to understanding human behavior. These applications encompass human-computer interaction, sensory tracking, 3D visual imaging, and human motion forecasting, among other aspects. Simultaneously, they provide invaluable datasets encapsulating a diverse array of human postures within authentic video sequences, contributing to the refinement and application of correlated algorithms and models. While current research has achieved notable breakthroughs in the accuracy of pose estimation and tracking, there has been a comparatively limited focus on efficient multi-person pose estimation and tracking amidst frame degradation [1, 2, 3]. This study introduces a feature sampling block design for the person detection stage, autonomously acquiring the ability to spatially sample features from supporting frames without additional supervision. This innovative approach presents a solution for striking a balance between accuracy and running speed in two-stage pose estimation and tracking schemes. The feature sampling block enhances adaptability, facilitating seamless integration with diverse object detection architectures.  
Most multi-person pose estimation and tracking methods can be categorized into two pipelines: bottom-up [4, 5] and top-down [6, 7, 8, 9]. Due to the absence of a global view of individual instances in the bottom-up pipeline, state-of-the-art top-down approaches excel in accuracy on large-scale benchmarks. However, their two-stage methodology tends to compromise inference speed compared to more streamlined bottom-up and single-stage approaches. Thus, the key challenge lies in designing a module that can effectively sample and exploit temporal information. Additionally, the final system in the tracking stage should be extremely lightweight and easy to implement.  
To address these concerns, we propose a straightforward yet highly efficient spatiotemporal module. By incorporating deformable convolutions in both spatial and temporal dimensions, our module effectively utilizes temporal information for person detection in videos. When integrated into the multi-person pose estimation and tracking framework, the module intelligently samples relevant feature information from neighboring video frames, significantly improving the accuracy of the current frame. Our spatiotemporal sampling framework is trained on an extensive dataset comprising annotated bounding boxes from a diverse collection of still images and video frames. In summary, our main contributions are as follows:  
* This paper introduces a sophisticated two-stage joint framework for human pose estimation and tracking. In the initial stage, a precise target detector is employed to detect human bodies in video frames using a top-down approach. Subsequently, human keypoint coordinates are obtained by combining heatmaps with offset regression. The second stage utilizes a bipartite graph matching algorithm to achieve keypoint tracking between consecutive video frames.
* This study pioneers a unified framework for human pose estimation and tracking, achieving commendable performance on widely adopted benchmarks.
* The framework exhibits remarkable scalability, allowing seamless integration into diverse human visual applications, such as 3D human pose estimation, human parsing, action reconstruction, and other related domains.