Robust 3D Ball Tracking in Team Sports using Multi-View Fusion and Enhanced ECO-Kalman Framework
Zhenguo Lu, Jing An, Xiaotao Li, Tiantao Song, Su Li
Journal of Multimedia Processing and Technologies, Vol. 16, No. 4, 2025.
DOI: https://doi.org/10.6025/jmpt/2025/16/4/184-192
Link: https://www.dline.info/jmpt/fulltext/v16n4/jmptv16n4_2.pdf
Abstract:
The paper presents a 3D ball tracking framework tailored for complex team sports environments, such as basketball and football, where challenges like occlusion, small object size, and dynamic backgrounds hinder accurate motion analysis. The proposed method integrates a small object detection network based on multiscale features with the Faster R-CNN architecture to improve 2D ball detection across multiple camera views. To address tracking drift and missed detections, the study enhances the ECO (Efficient Convolution Operators) tracking algorithm by incorporating cross view information via epipolar constraints and updating the tracking model using detection outputs. These refined 2D coordinates from synchronized cameras are then fused into 3D space using a triangulation algorithm, which computes accurate 3D ball positions while filtering out results with high reprojection errors. A simplified nonlinear motion model combined with a Kalman filter further smooths the 3D trajectory, yielding precise and stable tracking paths. Experiments on the APIDIS basketball dataset and self collected sports videos demonstrate significant improvements: 2D tracking accuracy rises from 0.67 to 0.77, and 3D tracking accuracy reaches 0.92 with the enhanced Kalman filter. The framework effectively addresses real-world issues such as occlusion, lighting variation, and camera calibration errors, providing a robust solution for sports video analysis. This approach supports advanced applications such as referee assistance, tactical evaluation, and performance analytics, highlighting its potential to advance intelligent systems in competitive sports..