Detekcija i praćenje igrača u nogometnim scenama

master's thesis

• Title (english): Detection and Tracking of Players in Soccer Scenes
• Author: Tomislav Kiš
• Mentor: Sven Lončarić (mentor)
• Committee member: Sven Lončarić (predsjednik povjerenstva)
• Committee member: Marko Subašić (član povjerenstva)
• Committee member: Tomislav Petković (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; Zagreb
• Defense date and country: 2017-07-11, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Computing

Abstract

Ovaj rad daje usporedbu efikasnosti Faster R-CNN modela i klasičnih metoda računalnog vida za problem detektiranja igrača u scenama. Ispitani modeli Faster R-CNN-a učeni su na dva različita skupova s označenim pješacima. Prikazani su rezultati, navedeni problemi i nedostaci te mogućnosti dorade. Klasičan pristup zasniva se na oduzimanju pozadine te dodatnom procesiranju detektiranih regija. Korištene su paralelne i optimirane funkcije iz biblioteke otvorenog koda OpenCV. Ispitani su parametri k-NN metode oduzimanja pozadine i morfoloških transformacija za otklanjanje uvjeta scene. Opisani su algoritmi praćenja i analize detektiranih igrača te rezultati i moguća poboljšanja.

Abstract (english)

In this thesis we compare efficiency of Faster R-CNN model and classical methods of computer vision for detecting players in soccer scenes. Tested models of Faster R-CNN were trained on two different pedestrian datasets. Results, potential problems and improvements were presented. Classical approach is based on background subtraction and further processing of detected regions. Parallel and optimized functions from OpenCV library were used. Parameters of k-NN background subtraction method and morphological transformations for scene conditions were tested. Algorithms for tracking and player analysis, and results and possible improvements are described.

• Keywords: duboko učenje; konvolucijske neuronske mreže; mreže za detekciju regija; Faster R-CNN; Caltech Pedestrian skup; Caltech Multispectral Pedestrian skup; OpenCV; oduzimanje pozadine; k-NN metoda; morfološke transformacije; detektiranje regija; analiza regija; razdvajanje igrača; Kalman filter
• Keywords (english): deep learning; convolutional neural networks; region based neural networks; Faster R-CNN; Caltech Pedestrian dataset; Caltech Multispectral Pedestrian dataset; OpenCV; background subtraction; k-NN method; morphological transformations; region detection; region analysis; players separation; Kalman filter
• Language: croatian
• URN:NBN: urn:nbn:hr:168:523213
• Study programme: Title: Computing; Study programme type: university; Study level: graduate; Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Closed access
• Created on: 2019-03-13 21:32:32