Object Tracking by Reconstruction with View-Specific Discriminative Correlation Filters
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Yksityiskohdat
| Alkuperäiskieli | Englanti |
|---|---|
| Otsikko | 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) |
| Kustantaja | IEEE |
| ISBN (elektroninen) | 978-1-7281-3293-8 |
| ISBN (painettu) | 978-1-7281-3294-5 |
| Tila | Julkaistu - kesäkuuta 2019 |
| OKM-julkaisutyyppi | A4 Artikkeli konferenssijulkaisussa |
| Tapahtuma | IEEE/CVF Conference on Computer Vision and Pattern Recognition - Kesto: 1 tammikuuta 2000 → … |
Julkaisusarja
| Nimi | IEEE/CVF Conference on Computer Vision and Pattern Recognition |
|---|---|
| ISSN (painettu) | 1063-6919 |
| ISSN (elektroninen) | 2575-7075 |
Conference
| Conference | IEEE/CVF Conference on Computer Vision and Pattern Recognition |
|---|---|
| Lyhennettä | CVPR |
| Ajanjakso | 1/01/00 → … |
Tiivistelmä
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.