Original title: Predicting targets in Multiple Object Tracking task
Translated title: Predicting targets in Multiple Object Tracking task
Authors: Citorík, Juraj ; Děchtěrenko, Filip (advisor) ; Brunetto, Robert (referee)
Document type: Master’s theses
Year: 2016
Language: eng
Abstract: [eng] [cze]
The aim of this thesis is to predict targets in a Multiple Object Tracking (MOT) task, in which subjects track multiple moving objects. We processed and analyzed data containing object and gaze position information from 1148 MOT trials completed by 20 subjects. We extracted multiple features from the raw data and designed a machine learning approach for the prediction of targets using neural networks and hidden Markov models. We assessed the performance of the models and features. The results of our experiments show that it is possible to train a machine learning model to predict targets with very high accuracy. 1 Cie©om tejto práce je určenie sledovaných cie©ov v úlohe sledovania viacerých objektov, pri ktorej účastník sleduje nieko©ko pohybujúcich sa objektov. Ana- lyzovali sme dáta pochádzajúce z 1148 pokusov vykonaných 20 účastníkmi. Z dát sme extrahovali rôzne príznaky a navrhli postup pre určovanie sledovaných cie©ov používajúci neurónové siete a skryté Markovove modely. Vykonali sme porovnanie úspešnosti týchto modelov a príznakov. Výsledky naznačujú že metódy strojového učenia umožňujú s ve©kou spo©ahlivos'ou určova' sledované ciele. 1
Keywords: eye movements; machine learning; Multiple Object Tracking; eye movements; machine learning; Multiple Object Tracking

Institution: Charles University Faculties (theses) (web)
Document availability information: Available in the Charles University Digital Repository.
Original record: http://hdl.handle.net/20.500.11956/83122

Permalink: http://www.nusl.cz/ntk/nusl-352771


The record appears in these collections:
Universities and colleges > Public universities > Charles University > Charles University Faculties (theses)
Academic theses (ETDs) > Master’s theses