Abstract
In this research we introduce a new labelled SportLogo dataset, that contains images of two kinds of sports: hockey (NHL) and basketball (NBA). This dataset presents several challenges typical for logo detection tasks. A huge number of occlusions and logo view changes during playing games lead to an ambiguity of a straightforward detection approach use. Another issue is logo style changes due to seasonal kits updates. In this paper we propose a two stage approach, in which, firstly, an input image is processed by a specially trained scene recognition convolutional neural network. Second, conventional object detectors are applied only for sport scenes. Experimental study contains results of different combinations of backbone and detector convolutional neural networks. It was shown that MobileNet + YOLO v3 solution provides the best quality results on the designed dataset (mAP = 0.74, Recall = 0.87).
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This research is based on the work supported by Samsung Research, Samsung Electronics.
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Kuznetsov, A., Savchenko, A.V. (2020). A New Sport Teams Logo Dataset for Detection Tasks. In: Chmielewski, L.J., Kozera, R., Orłowski, A. (eds) Computer Vision and Graphics. ICCVG 2020. Lecture Notes in Computer Science(), vol 12334. Springer, Cham. https://doi.org/10.1007/978-3-030-59006-2_8
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