Graph-Based Multi-Camera Soccer Player Tracker

TL;DR

This paper introduces a multi-camera soccer player tracking method that leverages player dynamics and interactions, operating directly on detection heat maps to improve accuracy despite calibration errors.

Contribution

It proposes a novel tracking approach that works directly on raw detection heat maps from multiple cameras, enhancing multi-view tracking robustness.

Findings

Effective in long shot soccer videos with calibration errors

Operates directly on detection heat maps, reducing reliance on appearance features

Trained on synthetic data and fine-tuned with real-world data

Abstract

The paper presents a multi-camera tracking method intended for tracking soccer players in long shot video recordings from multiple calibrated cameras installed around the playing field. The large distance to the camera makes it difficult to visually distinguish individual players, which adversely affects the performance of traditional solutions relying on the appearance of tracked objects. Our method focuses on individual player dynamics and interactions between neighborhood players to improve tracking performance. To overcome the difficulty of reliably merging detections from multiple cameras in the presence of calibration errors, we propose the novel tracking approach, where the tracker operates directly on raw detection heat maps from multiple cameras. Our model is trained on a large synthetic dataset generated using Google Research Football Environment and fine-tuned using real-world data to reduce costs involved with ground truth preparation.