## 2 Related Work  
In the early days, human pose estimation tasks primarily relied on graph structure models and deformable component models. These approaches required extensive prior knowledge and manually designed features, which often resulted in weak representation capabilities. [PERSON] et al. [11] reframed the human pose estimation problem as a regression task, employing deep convolutional neural networks for end-to-end key point regression. Since then, deep learning-based methods have become the mainstream approach for pose estimation. To improve detection accuracy, researchers have implemented techniques such as multi-level cascades [12] by effectively capturing both local appearance features and structured relationships between body parts. In addition, image pyramids [13] were incorporated to improve object localization by efficiently handling objects at different scales, enhancing the accuracy of predicting bounding boxes while maintaining computational efficiency. Furthermore, iterative optimization [14] was utilized to progressively refine human pose estimates by feeding-back prediction errors into the model, allowing for more accurate pose estimation through repeated corrections using convolutional neural networks (CNNs). [PERSON] et al. [15] were the first to use heat maps for human pose estimation, assuming key points follow a normal distribution and are spatially supervised. This approach simplifies network learning compared to direct regression, reducing its complexity. Although the accuracy of heat map-based methods has significantly improved compared to direct regression, these methods have their own limitations. First, they do not directly produce final key point coordinates, leading to quantization errors when converting the heat map to coordinates. Second, generating high-resolution heat maps is computationally expensive, which hinders the development of lightweight models. In recent years, various studies have addressed these limitations by enhancing regression methods while maintaining spatial generalization. For example, DSNT [16] introduced a soft-argmax operation to directly estimate coordinates from the feature map, implicitly learning the spatial distribution of key points and eliminating quantization errors. RLE [17] re-examined key point detection through the lens of maximum causality estimation and used flow models [18] to learn the true distribution of key points, enabling regression-based methods to achieve accuracy comparable to heat map-based approaches. CenterNet [19] detects human body instances using a center point heat map, then applies offset regression to generate all key points. SPM [20] further refines offset regression by converting long-range regression into short-range regression through a graded approach. In recent years, as research into video understanding tasks has deepened, behavior recognition studies and applications have proliferated. Behavioral actions can be recognized using various types of information, including human skeleton data, spatio-temporal information, optical flow, and temporal features. Since human behavior is a continuous process, but often only a few keyframes significantly influence action classification, abnormal action recognition can be approached as an analysis of these keyframes. Moreover, recognizing abnormal movements using human skeleton data can be framed as a human posture recognition task. For example, [PERSON] et al. extracted human joints using AlphaPose [21] and evaluated dance movements through skeleton similarity matching. This method simplifies implementation by avoiding large-scale data collection, but it relies heavily on accurate action template definitions and empirical analysis. [PERSON] et al. [22] captured the spatial and temporal aspects of static information during motion and trained a model for fusion, achieving strong performance. However, this approach is less effective for long-term video datasets, and optical flow extraction is computationally intensive, which hampers real-time performance. [PERSON] et al.