Vision Based Dynamic Offside Line Marker for Soccer Games
This addresses the need for accurate refereeing in soccer games, where false offside decisions can impact outcomes, but it appears incremental as it builds on existing vision methods.
The paper tackled the problem of automating offside detection in soccer to reduce controversial calls by using computer vision techniques like Hough transform and color similarity, resulting in a method to identify probable offside regions.
Offside detection in soccer has emerged as one of the most important decisions with an average of 50 offside decisions every game. False detections and rash calls adversely affect game conditions and in many cases drastically change the outcome of the game. The human eye has finite precision and can only discern a limited amount of detail in a given instance. Current offside decisions are made manually by sideline referees and tend to remain controversial in many games. This calls for automated offside detection techniques in order to assist accurate refereeing. In this work, we have explicitly used computer vision and image processing techniques like Hough transform, color similarity (quantization), graph connected components, and vanishing point ideas to identify the probable offside regions. Keywords: Hough transform, connected components, KLT tracking, color similarity.