Synthetic Local Data Augmentation

Modern object segmentation models are crucial in sports analytics, particularly in dynamic sports like hockey where fast-paced action often results in blurred imagery, such as motion-blurred hockey sticks. Given the shortage of segmentation data for uncommon objects like hockey sticks, data augmentation emerges as a natural solution to enhance training datasets. However, traditional data augmentation methods, which apply transformations at the image level, can distort critical relational cues between objects and their surroundings, undermining a model's ability to accurately segment objects in such challenging conditions. To address this, we propose the Synthetic Local Data Augmentation (SLDA) technique, which selectively applies traditional DA transformations-like scaling, rotation, blurring, and motion blur-directly to individual target objects. This technique allows precise customization of transformations to specifically enhance model robustness against particular types of distortions, such as the motion blur frequently observed with fast-moving hockey sticks. Utilizing a segmented dataset of hockey sticks, SLDA introduces a greater variety of stick instances by inserting elements in the scene with different examples of the same category. This focused approach significantly enhances the model's ability to recognize hockey sticks across a range of visual conditions, thereby improving its generalization capabilities. SLDA detailed experiments in a case study on hockey stick seg-mentation, we demonstrate how SLDA surpasses existing object-level and traditional data augmentation methods in promoting model robustness and adaptive precision. Surpassing alternative by 2.1 %, i.e. from 85% to 87% in F1 Score on small model complexity, and by 5.8%, i.e. from 86% to 92% in mAP50 on large model complexity.