Name
Abstract | ||
|---|---|---|
This paper adapts a popular image quality measure called structural similarity for high precision registration based tracking while also introducing a simpler and faster variant of the same. Further, these are evaluated comprehensively against existing measures using a unified approach to study registration based trackers that decomposes them into three constituent sub modules - appearance model, state space model and search method. Several popular trackers in literature are broken down using this method so that their contributions - as of this paper - are shown to be limited to only one or two of these submodules. An open source tracking framework is made available that follows this decomposition closely through extensive use of generic programming. It is used to perform all experiments on four publicly available datasets so the results are easily reproducible. This framework provides a convenient interface to plug in a new methodfor any sub module and combine it with existing methods for the other two. It can also serve as a fast and flexible solution for practical tracking needs due to its highly efficient implementation. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/WACV.2017.19 | 2017 IEEE Winter Conference on Applications of Computer Vision (WACV) |
Keywords | Field | DocType |
registration based tracking,structural similarity,image quality measure,appearance model,state space model,search method,open source tracking framework,generic programming | Data mining,BitTorrent tracker,Computer science,State-space representation,Image quality,Active appearance model,Structural similarity,Artificial intelligence,Plug-in,Source tracking,Generic programming,Machine learning | Conference |
ISSN | ISBN | Citations |
2472-6737 | 978-1-5090-4823-6 | 0 |
PageRank | References | Authors |
0.34 | 55 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Abhineet Singh | 1 | 13 | 3.60 |
| Mennatullah Siam | 2 | 36 | 7.06 |
| Martin Jägersand | 3 | 334 | 43.10 |