Computer Vision in Ice Hockey: Realistic Stick Augmentation and Virtual Game Reconstruction

Abstract

Ice hockey presents a uniquely challenging environment for computer vision due to its fast pace, heavy occlusions, motion blur, and the visual similarity of objects like sticks and players. Compounding these issues is a lack of annotated data, which limits the effectiveness of modern deep learning models. This thesis addresses two key problems in this domain: robust detection of hockey sticks under visual noise, and the generation of realistic, annotated video data to support model development and analytics. First, we introduce Synthetic Local Data Augmentation (SLDA), an instance-level augmentation strategy tailored for hockey stick segmentation. SLDA injects real segmented stick masks into broadcast images using context-aware transformations such as motion blur, geometric scaling, and lighting adjustments. Applied to a custom dataset of over 4,000 stick annotations, SLDA significantly improves segmentation accuracy on occluded and fast-moving sticks, yielding gains of up to +5.8% mAP@50 and +4.0% F1- score over strong baselines. Second, we present a Unity-based 3D Hockey Simulation Tool that reconstructs entire hockey game sequences from tabular puck and player coordinates. The simulator animates realistic gameplay with fully configurable cameras, enabling multi-angle video synthesis and precise ground-truth annotations. This tool is designed to support model training, evaluation, and visual analytics by converting coordinate logs into synchronized multi-view videos paired with dense per-frame annotations—player bounding boxes, player rink coordinates, segmentation masks, and full camera parameters (intrinsics and extrinsics). A full quantitative assessment of these uses is left for future work. Together, these contributions demonstrate how targeted augmentation and synthetic simulation can overcome real-world data limitations in sports vision. By combining SLDA’s image-level enhancements with full-scene reconstruction, this thesis lays a foundation for more robust, scalable, and intelligent systems in automated hockey analytics and beyond.