Real Time Multi-Object Detection for Helmet Safety
This work addresses helmet safety and injury monitoring for football players, specifically in the NFL, but it is incremental as it applies existing methods to a new dataset from a competition.
The paper tackles the problem of automatically tracking football player helmets and assigning helmet impacts to correct players using computer vision and machine learning algorithms, with the goal of accurately identifying each player's 'exposures' throughout a play to aid in injury surveillance and mitigation.
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.