Appearance based online visual object tracking
|
Kokul Thanikasalam Thesis
(PDF 6MB)
Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. |
Description
This thesis presents research contributions to the field of computer vision based visual object tracking. This study investigates appearance based object tracking by using traditional hand-crafted and deep features. The thesis proposes a real-time tracking framework with high accuracy which follows a deep similarity tracking strategy. This thesis also proposes several deep tracking frameworks for high-accuracy tracking and to manage the spatial information loss. The research findings of the study would be able to be used in a range of applications including visual surveillance systems.
Impact and interest:
Citation counts are sourced monthly from Scopus and Web of Science® citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.
Full-text downloads:
Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.
| ID Code: | 130875 |
|---|---|
| Item Type: | QUT Thesis (PhD) |
| Supervisor: | Fookes, Clinton & Sridharan, Sridha |
| Keywords: | Visual Object Tracking, Appearance Modelling, Similarity Matching, Model-Free Object Tracking, Deep Tracking, Deep Neural Network, Convolutional Neural Network, Real-time Tracking, Computer Vision, Image Processing |
| DOI: | 10.5204/thesis.eprints.130875 |
| Divisions: | Past > QUT Faculties & Divisions > Science & Engineering Faculty |
| Institution: | Queensland University of Technology |
| Deposited On: | 24 Jul 2019 02:35 |
| Last Modified: | 24 Jul 2019 02:35 |
Export: EndNote | Dublin Core | BibTeX
Repository Staff Only: item control page
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)