A computer vision framework for MotoGP rider posture estimation

This thesis presents Postura Pilota, a computer vision framework designed for analyzing MotoGP rider posture from onboard camera footage. Developed in collaboration with the Ducati MotoGP Team, the framework addresses the challenge of extracting a precise rider posture. The system employs a two-stage approach to overcome the difficulties of onboard MotoGP footage analysis. First, a video stabilization technique using YOLOv8s-OBB (Oriented Bounding Box) object detection identifies the DUCATI logo on the bike’s tail as a reference point, enabling frame-by-frame translation to compensate for camera movement and electronic image stabilization artifacts. This preprocessing step achieves 99.5% mAP@50 accuracy in logo detection and eliminates unwanted camera motion. Second, the framework implements instance and semantic segmentation through a dual-network approach. YOLOv8m-SEG performs instance segmentation to identify seven distinct body parts (head, torso, left/right arms, left/right legs, and motorcycle tail), achieving 97.7% mAP@50 for bounding box detection and 97.5% for mask segmentation. Additionally, a novel reconstruction component using MMSegmentation with PSPNet-ResNet101 architecture addresses the challenge of reconstruct segmentation map where body parts are not visible. Through a custom training methodology involving artificially obscured training images with complete ground truth labels, the network learns to infer missing anatomical information, achieving 98.80% overall accuracy with 90.86% mean IoU. The resulting segmentation maps provide comprehensive 2D posture data that enables comparative analysis between riders and supports aerodynamic studies, addressing the challenge that MotoGP riders significantly impact vehicle aerodynamic performance.