Jersey Number Recognition With Semi-Supervised Spatial Transformer Network

Gen Li, Shikun Xu, Xiang Liu, Lei Li, Changhu Wang; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2018, pp. 1783-1790

Abstract


It is still a challenging task to recognize the jersey number of players on the court in soccer match videos, as the jersey numbers are very small in the object detection task and annotated data are not easy to collect. Based on the object detection results of all the players on the court, a CNN model is first introduced to classify these numbers on the deteced players' images. To localize the jersey number more precisely without involving another digit detector and extra consumption, we then improve the former network to an end-to-end framework by fusing with the spatial transformer network (STN). To further improve the accuracy, we bring extra supervision to STN and upgrade the model to a semi-supervised multi-task learning system, by labeling a small portion of the number areas in the data set by quadrangle. Extensive experiments illustrate the effectiveness of the proposed framework.

Related Material


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[bibtex]
@InProceedings{Li_2018_CVPR_Workshops,
author = {Li, Gen and Xu, Shikun and Liu, Xiang and Li, Lei and Wang, Changhu},
title = {Jersey Number Recognition With Semi-Supervised Spatial Transformer Network},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2018}
}