Detection and Re-Identification in the case of Horse Racing

Despite its popularity and the substantial betting it attracts, horse racing has seen limited research in machine learning. Some studies have tackled related challenges, such as adapting multi-object tracking to the unique geometry of horse tracks and tracking jockey caps during complex manoeuvres. Our research aims to create a helmet detector framework as a preliminary step for re-identification using a limited dataset. Specifically, we detected jockeys' helmets throughout a 205-second race with six disjointed outdoor cameras, addressing challenges like occlusion and varying illumination. Occlusion is a significant challenge in horse racing, often more pronounced than in other sports. Jockeys race in close groups, causing substantial overlap between jockeys and horses in the camera’s view, complicating detection and segmentation. Additionally, motion blur, especially in the race's final stretch, and the multi-camera broadcast capturing various angles—front, back, and sides—further complicate detection and consecutively re-identification (Re-ID). To address these issues, we focus on helmet identification rather than detecting all horses or jockeys. We believe helmets, with their simple shapes and consistent appearance even when rotated, offer a more reliable target for detection to make the Re-ID downstream task more achievable.