Player detection in field sports

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Date

2018-02-01

Author

Direkoglu, Cem
Sah, Melike
O'Connor, Noel E.

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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We describe a method for player detection in field sports with a fixed camera setup based on a new player feature extraction strategy. The proposed method detects players in static images with a sliding window technique. First, we compute a binary edge image and then the detector window is shifted over the edge regions. Given a set of binary edges in a sliding window, we introduce and solve a particular diffusion equation to generate a shape information image. The proposed diffusion to generate a shape information image is the key stage and the main theoretical contribution in our new algorithm. It removes the appearance variations of an object while preserving the shape information. It also enables the use of polar and Fourier transforms in the next stage to achieve scale- and rotation-invariant feature extraction. A support vector machine classifier is used to assign either player or non-player class inside a detector window. We evaluate our approach on three different field hockey datasets. In general, results show that the proposed feature extraction is effective and performs competitive results compared to the state-of-the-art methods.

Subject Keywords

Feature extraction, Heat diffusion, Player detection, Field sports

URI

https://hdl.handle.net/11511/67283

Journal

MACHINE VISION AND APPLICATIONS

DOI

https://doi.org/10.1007/s00138-017-0893-8

Collections

Engineering, Article

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Citation Formats

C. Direkoglu, M. Sah, and N. E. O’Connor, “Player detection in field sports,” MACHINE VISION AND APPLICATIONS, pp. 187–206, 2018, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/67283.