YO-CSA-T: A Real-time Badminton Tracking System Utilizing YOLO Based on Contextual and Spatial Attention
Yuan Lai, Zhiwei Shi, Chengxi Zhu

TL;DR
This paper introduces YO-CSA-T, a real-time badminton shuttlecock tracking system that enhances detection accuracy and speed by integrating a novel YOLOv8s-based network with attention mechanisms and a 3D trajectory prediction framework.
Contribution
The paper proposes YO-CSA, a modified YOLOv8s model with attention modules, and a comprehensive real-time 3D trajectory detection system for badminton shuttlecocks, improving accuracy and efficiency.
Findings
YO-CSA achieves 90.43% [email protected], outperforming YOLOv8s and YOLO11s.
System maintains over 130 fps in real-time detection.
Effective 3D trajectory prediction and compensation improve tracking continuity.
Abstract
The 3D trajectory of a shuttlecock required for a badminton rally robot for human-robot competition demands real-time performance with high accuracy. However, the fast flight speed of the shuttlecock, along with various visual effects, and its tendency to blend with environmental elements, such as court lines and lighting, present challenges for rapid and accurate 2D detection. In this paper, we first propose the YO-CSA detection network, which optimizes and reconfigures the YOLOv8s model's backbone, neck, and head by incorporating contextual and spatial attention mechanisms to enhance model's ability in extracting and integrating both global and local features. Next, we integrate three major subtasks, detection, prediction, and compensation, into a real-time 3D shuttlecock trajectory detection system. Specifically, our system maps the 2D coordinate sequence extracted by YO-CSA into 3D…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
MethodsSoftmax · Attention Is All You Need · SPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
