PLayerTV: Advanced Player Tracking and Identification for Automatic Soccer Highlight Clips

TL;DR

PlayerTV is an AI-powered framework that automates player tracking and identification in soccer videos, enabling efficient creation of personalized highlight clips with minimal manual effort.

Contribution

The paper introduces PlayerTV, a novel system combining detection, OCR, and color analysis for automatic player identification in soccer videos, streamlining highlight generation.

Findings

Accurately identifies players and teams in real-world soccer footage.

Reduces manual labor in creating highlight clips.

Provides a user-friendly GUI for practical use.

Abstract

In the rapidly evolving field of sports analytics, the automation of targeted video processing is a pivotal advancement. We propose PlayerTV, an innovative framework which harnesses state-of-the-art AI technologies for automatic player tracking and identification in soccer videos. By integrating object detection and tracking, Optical Character Recognition (OCR), and color analysis, PlayerTV facilitates the generation of player-specific highlight clips from extensive game footage, significantly reducing the manual labor traditionally associated with such tasks. Preliminary results from the evaluation of our core pipeline, tested on a dataset from the Norwegian Eliteserien league, indicate that PlayerTV can accurately and efficiently identify teams and players, and our interactive Graphical User Interface (GUI) serves as a user-friendly application wrapping this functionality for streamlined use.