Automated Football Video Analysis System

Author(s)	Mr. Nehal Deepak Nemade
Country	India
Abstract	This paper presents an automated football video analysis system that detects and tracks the players, referees and the ball across video frames, and at the same time goes a step further by classifying the teams and flagging kick events on its own. The pipeline combines a YOLO-based object detector with ByteTrack multi-object tracking, a Kalman filter for smoothing the ball trajectory, KMeans color clustering for team classification and a rule-based kick event detector, whereas each module handles one specific part of the problem. There are two main contributions to discuss. The first is a speed-aware ball possession assignment approach, which reduces the false positives on high-velocity passes and kicks, and the second is an on-screen annotation pipeline that overlays the output video with player IDs, team colors, ball speed and kick events, so that a coach can review a complete match without re-watching it frame by frame.
Keywords	football video analysis, soccer analytics, computer vision, object detection, YOLOv8, multi-object tracking, ByteTrack, Kalman filter, team classification, KMeans clustering, ball possession detection, kick event detection, sports analytics
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 8, Issue 5, September-October 2026
Published On	2026-10-11
DOI	https://doi.org/10.36948/ijfmr.2026.v08i05.88593

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