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Original Article

JJCIT. 2019; 5(3): 181-194


Yan Chiew Chiew WONG.


Healthy lifestyle trends are getting more prominent globally around the world. There are numerous number of marathon running race events that have been held and inspired interest among peoples of different ages, genders and countries. Such diversified truths increase more difficulties to comprehending large number of marathon images since such process is often done manually. Therefore, a new approach for racing bib number (RBN) localization and recognition for marathon running races using deep learning is proposed in this paper. Previously all RBN application systems have been developed by using image processing techniques only and it limits the performance achieved. There are two phases in the proposed system that are phase 1: RBN detection, phase 2: RBN recognition. In phase 1, You Only Look Once version 3 (YOLOv3) which consists of single convolutional network is used to predict the runner and RBN by multiple bounding boxes and class probabilities of those boxes. YOLOv3 is a new classifier network that had outperforms other state-of-art networks. While in phase 2, Convolutional Recurrent Neural Network (CRNN) is used to generate a label sequence for each input image and then select the label sequence that has the highest probability. CRNN can be straight trained from sequence labels such as words without any annotation of characters. Therefore, CRNN recognizes the contents of RBN detected. The experimental results based on mean average precision (mAP) and edit distance have been analysed. The developed system suitable for marathon or distance running race events and automates the localization and recognition of racers, increasing the efficiency in event's control and monitoring and also post processing the event's data.

Key words: Racing Bib Number, You Only Look Once Version 3, Convolutional Recurrent Neural Network

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