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Multi-player detection in soccer broadcast videos using a blob-guided particle swarm optimization method

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Abstract

Soccer is the most popular sport around the world, and automatic processing of soccer images is a precious alternative to the manual solutions regarding the explosive growth of soccer videos. A new multi-player detection algorithm in far view frames as an initial step to a wide range of applications, such as player tracking, is addressed in this paper. In the proposed detector, a two-step blob detection (grass-based blob detection followed by an edge-based blob detection) is combined with an efficient search mechanism based on particle swarm optimization (PSO) by assigning sub-swarms to each detected blob. Then, a sub-swarm is initialized and tripled to search for three models corresponding to two teams and the referee. Therefore, the most player-like regions in detected blobs are simultaneously searched by all sub-swarms flying through the solution space, thus expanding the scope of single player detection to multi-player detection. Experimental results demonstrate the efficiency and robustness of the algorithm.

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Authors and Affiliations

  1. Department of Photogrammetry and Remote Sensing, K.N. Toosi University of Technology, Valieasr Street, Tehran, Iran

    M. Manafifard & H. Ebadi

  2. Department of Electrical Engineering, K. N. Toosi University of Technology, Seyedkhandan Street, Tehran, Iran

    H. Abrishami Moghaddam

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  1. M. Manafifard
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  2. H. Ebadi
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  3. H. Abrishami Moghaddam
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Correspondence to M. Manafifard.

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Manafifard, M., Ebadi, H. & Moghaddam, H.A. Multi-player detection in soccer broadcast videos using a blob-guided particle swarm optimization method. Multimed Tools Appl 76, 12251–12280 (2017). https://doi.org/10.1007/s11042-016-3625-6

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