A Survey of Advanced Computer Vision Techniques for Sports
원문 초록
Computer Vision developments are enabling significant advances in many fields, including sports. Many applications built on top of Computer Vision technologies, such as tracking data, are nowadays essential for every top-level analyst, coach, and even player. In this paper, we survey Computer Vision techniques that can help many sports-related studies gather vast amounts of data, such as Object Detection and Pose Estimation. We provide a use case for such data: building a model for shot speed estimation with pose data obtained using only Computer Vision models. Our model achieves a correlation of 67%. The possibility of estimating shot speeds enables much deeper studies about enabling the creation of new metrics and recommendation systems that will help athletes improve their performance, in any sport. The proposed methodology is easily replicable for many technical movements and is only limited by the availability of video data.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
A Survey on Performance Metrics for Object-Detection Algorithms
Beyond One Shot, Beyond One Perspective: Cross-View and Long-Horizon Distillation for Better LiDAR Representations
MambaFusion: Height-Fidelity Dense Global Fusion for Multi-modal 3D Object Detection
Weakly-supervised Contrastive Learning with Quantity Prompts for Moving Infrared Small Target Detection
Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation
Seg-R1: Segmentation Can Be Surprisingly Simple with Reinforcement Learning
토론
이 논문과 연결된 포럼 글이 아직 없습니다.