Name
Title | ||
|---|---|---|
Object Tracking By Reconstruction With View-Specific Discriminative Correlation Filters |
Abstract | ||
|---|---|---|
Standard RGB-D trackers treat the target as a 2D structure, which makes modelling appearance changes related even to out-of-plane rotation challenging. This limitation is addressed by the proposed long-term RGB-D tracker called OTR - Object Tracking by Reconstruction. OTR performs online 3D target reconstruction to facilitate robust learning of a set of view-specific discriminative correlation filters (DCFs). The 3D reconstruction supports two performance-enhancing features: (i) generation of an accurate spatial support for constrained DCF learning from its 2D projection and (ii) point-cloud based estimation of 3D pose change for selection and storage of view-specific DCFs which robustly localize the target after out-of-view rotation or heavy occlusion. Extensive evaluation on the Princeton RGB-D tracking and STC Benchmarks shows OTR outperforms the state-of-the-art by a large margin. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/CVPR.2019.00143 | 2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019) |
Field | DocType | Volume |
BitTorrent tracker,Pattern recognition,Computer science,Robust learning,Video tracking,Correlation,RGB color model,Artificial intelligence,Point cloud,Discriminative model,3D reconstruction | Journal | abs/1811.10863 |
ISSN | Citations | PageRank |
1063-6919 | 2 | 0.36 |
References | Authors | |
17 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ugur Kart | 1 | 2 | 0.70 |
| Alan Lukezic | 2 | 245 | 9.62 |
| Matej Kristan | 3 | 960 | 47.02 |
| Joni-Kristian Kämäräinen | 4 | 113 | 23.78 |
| José Matas | 5 | 2479 | 209.55 |