Self-Supervised Small Soccer Player Detection and Tracking

TL;DR

This paper introduces a self-supervised method for detecting and tracking small soccer players in videos, overcoming challenges of low resolution and similar appearances without requiring annotated training data.

Contribution

The authors propose a novel self-supervised pipeline for soccer player detection and tracking that performs well on small, low-resolution players without ground-truth annotations.

Findings

Achieves top-tier detection and tracking results on challenging soccer videos.

Outperforms several state-of-the-art methods in small player scenarios.

Effective in various recording conditions without annotated training data.

Abstract

In a soccer game, the information provided by detecting and tracking brings crucial clues to further analyze and understand some tactical aspects of the game, including individual and team actions. State-of-the-art tracking algorithms achieve impressive results in scenarios on which they have been trained for, but they fail in challenging ones such as soccer games. This is frequently due to the player small relative size and the similar appearance among players of the same team. Although a straightforward solution would be to retrain these models by using a more specific dataset, the lack of such publicly available annotated datasets entails searching for other effective solutions. In this work, we propose a self-supervised pipeline which is able to detect and track low-resolution soccer players under different recording conditions without any need of ground-truth data. Extensive quantitative and qualitative experimental results are presented evaluating its performance. We also present a comparison to several state-of-the-art methods showing that both the proposed detector and the proposed tracker achieve top-tier results, in particular in the presence of small players.