The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Fusing points and lines for high performance tracking
Abstract
This paper addresses the problem of real-time 3D model-based tracking by combining point-based and edge-based tracking systems. We present a careful analysis of the properties of these two sensor systems and show that this leads to some non -trivial design choices that collectively yield extremely high performance. In particular, we present a method for integrating the two systems and robustly combining the pose estimates they produce. Further we show how on-line learning can be used to improve the performance of feature tracking. Finally, to aid real-time performance, we introduce the FAST feature detector which can perform full-frame feature detection at 400Hz. The combination of these techniques results in a system which is capable of tracking average prediction errors of 200 pixels. This level of robustness allows us to track very rapid motions, such as 50deg camera shake at 6Hz
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Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
Optimization by Simulated Annealing
Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983) · Science · doi:10.1126/science.220.4598.671 · Cited by 45,134
A Combined Corner and Edge Detector
Harris, C., & Stephens, M. (1988) · Procedings of the Alvey Vision Conference 1988 · doi:10.5244/c.2.23 · Cited by 12,901
SUSAN—a new approach to low level image processing
Smith, S. M., & Brady, J. M. (1997) · International Journal of Computer Vision · doi:10.1023/a:1007963824710 · Cited by 3,333
MLESAC: A New Robust Estimator with Application to Estimating Image Geometry
Torr, P. H. S., & Zisserman, A. (2000) · Computer Vision and Image Understanding · doi:10.1006/cviu.1999.0832 · Cited by 2,396
Real-time simultaneous localisation and mapping with a single camera
Davison (2003) · Proceedings Ninth IEEE International Conference on Computer Vision · doi:10.1109/iccv.2003.1238654 · Cited by 1,733
Real-time visual tracking of complex structures
Drummond, T., & Cipolla, R. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2002.1017620 · Cited by 598
Self-citationStable real-time 3D tracking using online and offline information
Vacchetti, L., Lepetit, V., & Fua, P. (2004) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2004.92 · Cited by 328
Robust model-based motion tracking through the integration of search and estimation
Lowe, D. G. (1992) · International Journal of Computer Vision · doi:10.1007/bf00127170 · Cited by 310
Cited by
Machine Learning for High-Speed Corner Detection
Rosten, E., & Drummond, T. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744023_34 · Cited by 4,555
Self-citationAdaptive and Generic Corner Detection Based on the Accelerated Segment Test
Mair, E., Hager, G. D., Burschka, D., Suppa, M., & Hirzinger, G. (2010) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-15552-9_14 · Cited by 477
Recent progress in semantic image segmentation
Liu, X., Deng, Z., & Yang, Y. (2019) · Artificial Intelligence Review · doi:10.1007/s10462-018-9641-3 · Cited by 455
Monocular‐SLAM–based navigation for autonomous micro helicopters in GPS‐denied environments
Weiss, S., Scaramuzza, D., & Siegwart, R. (2011) · Journal of Field Robotics · doi:10.1002/rob.20412 · Cited by 414
Eade, E., & Drummond, T. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (CVPR'06) · doi:10.1109/cvpr.2006.263 · Cited by 396
Self-citation
Metadata
- Authors
- Rosten, E., & Drummond, T.
- Venue
- Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1
- Publisher
- IEEE
- Published
- 2005
- Pages
- 1508-1515 Vol. 2
- OpenAlex
- W2111589119
- Cited by
- 1,119
- References
- 17
- FWCI
- 16.67
- Type
- conference paper
- Language
- en
- Open access
- closed
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