Journal of Multimedia Information System

Korea Multimedia Society (한국멀티미디어학회)
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Tracking Players in Broadcast Sports

  • Received : 2018.07.26
  • Accepted : 2018.08.28
  • Published : 2018.12.31
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Abstract

Over the years application of computer vision techniques in sports videos for analysis have garnered interest among researchers. Videos of sports games like basketball, football are available in plenty due to heavy popularity and coverage. The goal of the researchers is to extract information from sports videos for analytics which requires the tracking of the players. In this paper, we explore use of deep learning networks for player spotting and propose an algorithm for tracking using Kalman filters. We also propose an algorithm for finding distance covered by players. Experiments on sports video datasets have shown promising results when compared with standard techniques like mean shift filters.

Keywords

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Fig. 1. Model Architecture

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Fig. 2. The background image that has to be mapped to the top view of the football field.

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Fig. 3. The 8 partitions divided into unique colors that represents the 8 different homographies.

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Fig. 4: Figures showing detection and team classification of players using our mode

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Fig. 5: Figures showing the player detection using background subtraction model and our model.

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Fig. 6: Figures showing the player detection using background subtraction model and our model.

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Fig. 7: Sample frames depicting Kalman filter player tracking.

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Fig. 8: Error comparison between mean shift model and our model architecture to the Mean shift tracking Algorithm to show the error with respect to time.

Algorithm 1 Player Tracking

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Algorithm 2 Finding the Actual Distance moved by the player

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