Real Time Multi-Object Detection for Helmet Safety

TL;DR

This paper presents a computer vision-based machine learning approach for real-time multi-object detection and tracking of helmets in football games to improve injury surveillance and player exposure analysis.

Contribution

It introduces a novel method for automatically tracking helmets and their collisions, enabling accurate player identification and exposure assessment during gameplay.

Findings

Effective helmet detection and tracking in real-time

Improved accuracy in assigning impacts to players

Facilitates analysis of injury exposure trends

Abstract

The National Football League and Amazon Web Services teamed up to develop the best sports injury surveillance and mitigation program via the Kaggle competition. Through which the NFL wants to assign specific players to each helmet, which would help accurately identify each player's "exposures" throughout a football play. We are trying to implement a computer vision based ML algorithms capable of assigning detected helmet impacts to correct players via tracking information. Our paper will explain the approach to automatically track player helmets and their collisions. This will also allow them to review previous plays and explore the trends in exposure over time.