Openly accessible

Video anomaly detection using deep generative models

Vu, Hung Thanh 2019, Video anomaly detection using deep generative models, Ph.D. thesis, Applied Artificial Intelligence Institute, Deakin University.

Attached Files
Name Description MIMEType Size Downloads
vu-videoanomaly-2019.pdf Connect to thesis application/pdf 16.38MB 3

Title Video anomaly detection using deep generative models
Author Vu, Hung Thanh
Institution Deakin University
School Applied Artificial Intelligence Institute
Faculty Applied Artificial Intelligence Institute
Degree type Research doctorate
Degree name Ph.D.
Thesis advisor Phung, DinhORCID iD for Phung, Dinh orcid.org/0000-0002-9977-8247
Date submitted 2019-11-30
Summary Video anomaly detection faces three challenges: a) no explicit definition of abnormality; b) scarce labelled data and c) dependence on hand-crafted features. This thesis introduces novel detection systems using unsupervised generative models, which can address the first two challenges. By working directly on raw pixels, they also bypass the last.
Language eng
Indigenous content off
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
Description of original 166 p.
Copyright notice ┬ęThe author
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30150108

Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 0 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 8 Abstract Views, 6 File Downloads  -  Detailed Statistics
Created: Fri, 16 Apr 2021, 16:07:30 EST by Bec Miller

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.