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Marathon athletes number recognition model with compound deep neural network

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Abstract

A large number of photos are taken for each athlete during a marathon competition, therefore, how to classify photos of specific athletes accurately and effectively has become the focus of attention. In this paper, we propose a compound deep neural network for marathon athletes number recognition to make classification more efficient and accurate. The proposed model is divided into three modules: image preprocessing module, text detection module, and text recognition module. Firstly, in the preprocessing module, we make use of the You Only Look Once version 3, and set the detection threshold and similarity threshold to reduce unnecessary detection. Secondly, we combine the efficient text detector Connectionist Text Proposal Network and the excellent text recognition general framework Convolutional Recurrent Neural Network (CRNN) to recognize the athletes number plates. Besides, to improve the accuracy of detection, we use transfer learning to fine-tune the CRNN. Finally, we design an effective tree filtering algorithm to avoid the interference caused by the text detection module. It can filter out invalid results, thereby improving the accuracy of the model. Our model is capable of performing classification on photos of marathon athletes with high precision. The model is feasible and effective, as indicated by the experiment results.

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Acknowledgements

This research was supported by Scientific and Technological Development Program Foundation of Jilin Province, China (Nos. 201604054YY; 20170414006GH).

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Authors and Affiliations

  1. College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge, Jilin University, Changchun, China

    Xin Wang

  2. College of Computer Science and Technology, Jilin University, Changchun, China

    Junxiang Yang

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  1. Xin Wang
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  2. Junxiang Yang
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Correspondence to Xin Wang.

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Wang, X., Yang, J. Marathon athletes number recognition model with compound deep neural network. SIViP 14, 1379–1386 (2020). https://doi.org/10.1007/s11760-020-01677-5

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  • DOI: https://doi.org/10.1007/s11760-020-01677-5

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