Human activity recognition using conditional random fields and privileged information

Abstract

This thesis solves the problem of human activity recognition from video sequences and static images, which is a part of computer vision field. To model human activities, conditional random fields were applied using data from heterogeneous sources. Moreover, a novel classification scheme that is based on the learning using privileged information was also proposed, where privileged information is given as an additional input to the classification model and it is available only during training but never during testing. Experimental results demonstrated that privileged information helps to build a stronger classifier than one would learn without it, while it significantly increases the recognition accuracy of the model.
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Handle URL
http://hdl.handle.net/10442/hedi/42814
ND
42814
Alternative title
Αναγνώριση ανθρώπινης δραστηριότητας με υπό συνθήκη τυχαία πεδία και προνομιακή πληροφορία
Author
Vrigkas, Michail Stefanos
Date
2016
Degree Grantor
University of Ioannina
Committee members
Νίκου Χριστόφορος
Κακαδιάρης Ιωάννης
Κόντης Λυσίμαχος - Παύλος
Λύκας Αριστείδης
Μπλέκας Κωνσταντίνος
Αργυρός Αντώνιος
Μπεμπής Γεώργιος
Discipline
Natural Sciences
Computer and Information Sciences
Engineering & Technology
Electrical Engineering, Electronic Engineering, Information Engineering
Keywords
human activity recognition; Privileged information; Conditional random fields
Country
Greece
Language
English
Description
9, xix, 183 σ., im., tbls., fig., ch.
Rights and terms of use
Το έργο παρέχεται υπό τους όρους της δημόσιας άδειας του νομικού προσώπου Creative Commons Corporation:Creative Commons Αναφορά Δημιουργού 3.0 Ελλάδα