Deep learning-based racing bib number detection and recognition

Abstract EN

Healthy lifestyle trends are getting more prominent around the world.

there are numerous numbers 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 numbers 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, which limits the performance achieved.

there are two phases in the proposed system that are phase 1: RBN detection and phase 2: RBN recognition.

in phase 1, you only look once version 3 (YOLOv3) which consists of a 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 outperforms other state-of-art networks.

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 analyzed.

the developed system is suitable for marathon or distance running race events and automates the localization and recognition of racers, thereby increasing efficiency in event control and monitoring as well as post-processing the event data.