Racing Bib Number Recognition Method using Deep Learning

 (*)Muhammad Aditya Rayhan Mail (Telkom University, Bandung, Indonesia)
 Kemas Muslim Lhaksmana (Telkom University, Bandung, Indonesia)

(*) Corresponding Author

DOI: http://dx.doi.org/10.30865/mib.v4i3.2270

Abstract

Mass running event has gained popularity ever since recreational running becomes more common as they often held annually by various organizers. As image documentation took a huge part to showcase the event, many thousands of images were generated during the event. Along with thousands of images that were generated, the participant is unlikely to found an image of themselves. To solve this problem, image annotation could be performed to address image with specific tags such as participant attribute like racing bib number (RBN). Manually annotate thousands of images would result in inefficiency of time and hard-labor. As a work to tackle this problem, this paper proposed an automatic image annotation system using the YOLOv3 algorithm based RBN recognition method. The experiment result shows 83.0% precision, 81.5% recall, and 82.2% F1 score as a result of our proposed method on running event dataset. Therefore, this implemented method will promote efficiency to solve the image annotation problem because it doesn't require manual annotation over thousand of running event images

Keywords


Racing Bib Number, Object Detection, Automatic Annotation, CNN, Object Recognition

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