## 2 Literature Review
### Research Progress in Fixation Point Coordinate Transformation  
Coordinate transformation is a key aspect of eye-tracking research, ensuring that fixation points accurately correspond to viewing content. However, balancing geometric accuracy, stability, and computational efficiency remains a major challenge. Geometric Projection-Based Methods were among the earliest widely adopted techniques, with their core principle being the construction of a geometric relationship between the eyeball, camera, and screen perspectives to maintain high-accuracy fixation estimation. [PERSON] and [PERSON] [52] established a projection model that allows stable remote fixation estimation in fixed scenarios, permitting a certain degree of head movement. However, this method is highly dependent on hardware calibration, and estimation accuracy significantly declines when participants move beyond the calibration range [53]. To reduce hardware dependency, Homography Matrix-Based Methods were introduced to address multi-view and geometric distortions through mathematical models [53]. This method calculates the mapping relationship between fixation points using 2D planar homography, improving its adaptability and making it applicable to scenarios with different viewing angles. Although this approach achieves high experimental accuracy (within 0.6\({}^{\circ}\)) and greater stability, its reliance on homography transformations and matrix operations may impose computational burdens in real-time applications. Additionally, in dynamic scenarios, head movements and environmental changes may affect its stability [54]. With advancements in image processing technology, researchers have explored Feature-Based Mapping Methods that do not require geometric modeling. [PERSON] [55] proposed the Scale-Invariant Feature Transform (SIFT), which establishes coordinate correspondence through image feature point matching, eliminating the need for traditional hardware-dependent approaches. However, SIFT is susceptible to lighting changes, occlusions, and the depth of field in dynamic scenes, leading to decreased matching stability [56]. Additionally, its high computational cost limits its feasibility for real-time applications [57]. Considering the developments in these methods, current coordinate transformation techniques still involve trade-offs among geometric modeling accuracy, head movement adaptability, dynamic scene stability, and computational efficiency. On this basis, a geometric point-line distance calculation approach is adopted in this study, and the coordinate transformation mechanism is optimized to reduce hardware calibration dependence while enhancing computational efficiency, providing a more flexible solution for eye-tracking research. In the context of visual cognition, the spatial accuracy of fixation coordinates is fundamental to reconstructing attentional patterns and viewing strategies. A precise transformation mechanism preserves the spatial and temporal coherence of gaze behavior, which is critical for interpreting how attention is distributed and how visual transitions occur across regions of interest. Conversely, mapping inaccuracies can disrupt the inferred structure of visual exploration and diminish the reliability of subsequent cognitive interpretations.