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Cited by 328

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

Stable real-time 3D tracking using online and offline information

Open access
Vacchetti, L., Lepetit, V., Fua, P.·2004·IEEE Transactions on Pattern Analysis and Machine Intelligence

Abstract

We propose an efficient real-time solution for tracking rigid objects in 3D using a single camera that can handle large camera displacements, drastic aspect changes, and partial occlusions. While commercial products are already available for offline camera registration, robust online tracking remains an open issue because many real-time algorithms described in the literature still lack robustness and are prone to drift and jitter. To address these problems, we have formulated the tracking problem in terms of local bundle adjustment and have developed a method for establishing image correspondences that can equally well handle short and wide-baseline matching. We then can merge the information from preceding frames with that provided by a very limited number of keyframes created during a training stage, which results in a real-time tracker that does not jitter or drift and can deal with significant aspect changes.

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Metadata

Authors
Vacchetti, L., Lepetit, V., & Fua, P.
Venue
IEEE Transactions on Pattern Analysis and Machine Intelligence
Published
2004-10
Volume
26
Issue
10
Pages
1385-1391
ISSN
0162-8828 (ISSN-L)
OpenAlex
W2108940975
Cited by
328
References
6
FWCI
23.44
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Stable real-time 3D tracking using online and offline information. https://app.cochat.ai/papers/10.1109/tpami.2004.92
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Vacchetti, L., Lepetit, V., & Fua, P. (2004). Stable real-time 3D tracking using online and offline information. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.2004.92

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.