Object tracking via non-Euclidean geometry: A Grassmann approach

Shiraz, Sareh, Harandi, Mehrtash T., Lovell, Brian C. and Sanderson, Conrad (2014). Object tracking via non-Euclidean geometry: A Grassmann approach. In: 2014 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE Winter Conference on Applications of Computer Vision (WACV 2014), Steamboat Springs, CO, United States, (901-908). 24-26 March 2014. doi:10.1109/WACV.2014.6836008

Attached Files (Some files may be inaccessible until you login with your UQ eSpace credentials)
Name Description MIMEType Size Downloads

Author Shiraz, Sareh
Harandi, Mehrtash T.
Lovell, Brian C.
Sanderson, Conrad
Title of paper Object tracking via non-Euclidean geometry: A Grassmann approach
Conference name IEEE Winter Conference on Applications of Computer Vision (WACV 2014)
Conference location Steamboat Springs, CO, United States
Conference dates 24-26 March 2014
Convener IEEE
Proceedings title 2014 IEEE Winter Conference on Applications of Computer Vision (WACV)   Check publisher's open access policy
Journal name 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014   Check publisher's open access policy
Series IEEE Workshop on Applications of Computer Vision. Proceedings
Place of Publication Piscataway, NJ, United States
Publisher Institute of Electrical and Electronics Engineers
Publication Year 2014
Year available 2014
Sub-type Fully published paper
DOI 10.1109/WACV.2014.6836008
Open Access Status
ISBN 9781479949854
9781479949847
ISSN 1550-5790
Start page 901
End page 908
Total pages 8
Language eng
Abstract/Summary A robust visual tracking system requires an object appearance model that is able to handle occlusion, pose, and illumination variations in the video stream. This can be difficult to accomplish when the model is trained using only a single image. In this paper, we first propose a tracking approach based on affine subspaces (constructed from several images) which are able to accommodate the abovementioned variations. We use affine subspaces not only to represent the object, but also the candidate areas that the object may occupy. We furthermore propose a novel approach to measure affine subspace-to-subspace distance via the use of non-Euclidean geometry of Grassmann manifolds. The tracking problem is then considered as an inference task in a Markov Chain Monte Carlo framework via particle filtering. Quantitative evaluation on challenging video sequences indicates that the proposed approach obtains considerably better performance than several recent state-of-the-art methods such as Tracking-Learning-Detection and MILtrack.
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

 
Versions
Version Filter Type
Citation counts: Scopus Citation Count Cited 9 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Tue, 12 Aug 2014, 12:23:51 EST by System User on behalf of School of Information Technol and Elec Engineering