Fisheye cameras, with their ultra-wide field-of-view (FOV) characteristics, are highly valuable for applications such as intelligent surveillance requiring large-scale human monitoring. However, human pose estimation in top-down fisheye images faces two critical challenges: first, the radial distribution of human targets caused by wide-angle imaging demands multi-directional annotated data due to the lack of rotational invariance in traditional algorithms; second, the nonlinear distortion of fisheye cameras dynamically varies with spatial positions, creating strong correlations between geometric deformation and imaging locations that existing methods fail to model effectively. To address these limitations, we propose FishPoseNet, a distortion-adaptive pose estimation framework based on YOLOv8Pose. Our framework includes three core innovations: (i) a Geometric Anchor Alignment Module (GAAM) that standardizes human orientations via affine transformations, enabling full-scene directional coverage with single-direction annotated samples; (ii) a position-sensitive Dynamic Distortion Coefficient Estimator (DDCE) establishing continuous mapping from pixel coordinates to distortion coefficients; (iii) an enhanced Distortion-Aware YOLOPose (DA-YOLOPose) network that leverages distortion coefficients to guide feature fusion, improving adaptability to nonlinear deformations. Experimental results on a self-built top-down fisheye surveillance dataset demonstrate significant improvements, offering an innovative solution for high-precision human pose estimation in complex distortion scenarios.