SkiTrack: An Aerial Skiing Benchmark for Human-Centric Object Tracking

Aerial skiing is a challenging human-centric sport characterized by rapid motion, large-scale variations, and frequent occlusions. Its extensive spatial range is typically captured by cameras or drones from multiple perspectives, resulting in frequent and complex viewpoint shifts. These challenges encompass nearly all difficulties inherent in human-centric tracking tasks. In this paper, we introduce SkiTrack , the first dataset explicitly designed for tracking in aerial skiing. SkiTrack enhances the performance of existing tracking algorithms across a range of human-centric scenarios by providing precise annotations. We observe distinct characteristics in the tracked components, with the skis being rigid and low in visibility and the athlete’s body highly deformable but more visible. To leverage these differences, we propose a components decoupled loss that applies separate constraints to the tracking of the athlete and skis, thereby improving tracking accuracy in skiing scenes. Our experimental results validate the effectiveness of both the SkiTrack dataset and the proposed decoupled loss function, demonstrating consistent improvements in the performance of established models on human-centric tracking tasks. Data is available at https://github.com/xiaozhangfangyang/FineSkiing .