Thesis Details
Automatické vyhodnocení záznamu cílové kamery
This thesis addresses the automation of photo finish evaluation in athletics. The detection of athletes in the target photo has been performed using the OpenPose library. Subsequently, athlete background segmentation has been performed to remove noise and cropping for individual athletes. The evaluation itself has been solved using a regression convolutional neural network. The accuracy of 77.60% has been achieved trough detecting the figures in the photo and 89.28% of the evaluated records has reached the accuracy lesser than 10 ms. The main benefit of this thesis is for novice photo finish referees, since they will have the evaluated target record available to them beforehand. Another usage serves for coaches and competitors, since they will be able to easily validate the evaluated records by themselves.
Photo finish evaluation, photo finish segmentation, Accurate photo finish system, Regression convolutional neural networks, Machine learning, Background segmentation
Bařina David, Ing., Ph.D. (DCGM FIT BUT), člen
Burget Radek, doc. Ing., Ph.D. (DIFS FIT BUT), člen
Češka Milan, doc. RNDr., Ph.D. (DITS FIT BUT), člen
Jaroš Jiří, doc. Ing., Ph.D. (DCSY FIT BUT), člen
@bachelorsthesis{FITBT24066, author = "Vojt\v{e}ch Jahoda", type = "Bachelor's thesis", title = "Automatick\'{e} vyhodnocen\'{i} z\'{a}znamu c\'{i}lov\'{e} kamery", school = "Brno University of Technology, Faculty of Information Technology", year = 2021, location = "Brno, CZ", language = "czech", url = "https://www.fit.vut.cz/study/thesis/24066/" }