Open AccessBenderNet and RingerNet: Highly Efficient Line Segmentation Deep Neural Network Architectures for Ice Rink Localization
Author(s) -
Pascale Walters,
David A. Clausi,
Alexander Wong,
Mehrnaz Fani
Publication year - 2021
Publication title -
journal of computational vision and imaging systems
Language(s) - English
Resource type - Journals
ISSN - 2562-0444
DOI - 10.15353/jcvis.v6i1.3535
Subject(s) - computer science , segmentation , artificial intelligence , artificial neural network , ice hockey , market segmentation , deep neural networks , line (geometry) , architecture , computer vision , geography , medicine , geometry , mathematics , archaeology , marketing , business , physical medicine and rehabilitation
A critical step for computer vision-driven hockey ice rink localization from broadcast video is the automatic segmentation of lines on the rink. While the leveraging of segmentation methods for sports field localization has been previously explored, the design of deep neural networks for segmenting ice rink lines has not been well studied. Furthermore, the exploration of efficient architecture designs is very important given the operational requirements of real-time sports analytics. Motivated by this, BenderNet and RingerNet, two highly efficient deep neural network architectures, have been designed specifically for ice rink line segmentation. Experiments on a dataset of annotated NHL broadcast video demonstrate high accuracy while maintaining high model efficiency, thus making the proposed methods well-suited for real-time ice hockey rink localization.