The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Visual tracking with online Multiple Instance Learning
Abstract
In this paper, we address the problem of learning an adaptive appearance model for object tracking. In particular, a class of tracking techniques called “tracking by detection” have been shown to give promising results at real-time speeds. These methods train a discriminative classifier in an online manner to separate the object from the background. This classifier bootstraps itself by using the current tracker state to extract positive and negative examples from the current frame. Slight inaccuracies in the tracker can therefore lead to incorrectly labeled training examples, which degrades the classifier and can cause further drift. In this paper we show that using Multiple Instance Learning (MIL) instead of traditional supervised learning avoids these problems, and can therefore lead to a more robust tracker with fewer parameter tweaks. We present a novel online MIL algorithm for object tracking that achieves superior results with real-time performance.
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Citations
Cites
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731
A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466
Rapid object detection using a boosted cascade of simple features
Viola, P., & Jones, M. (2005) · Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001 · doi:10.1109/cvpr.2001.990517 · Cited by 18,300
Friedman, J., Hastie, T., & Tibshirani, R. (2000) · The Annals of Statistics · doi:10.1214/aos/1016218223 · Cited by 6,884
Incremental Learning for Robust Visual Tracking
Ross, D. A., Lim, J., Lin, R.-S., & Yang, M.-H. (2008) · International Journal of Computer Vision · doi:10.1007/s11263-007-0075-7 · Cited by 3,061
Self-citationReal-time tracking of non-rigid objects using mean shift
Comaniciu, D., Ramesh, V., & Meer, P. (2002) · Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662) · doi:10.1109/cvpr.2000.854761 · Cited by 2,906
Solving the multiple instance problem with axis-parallel rectangles
Dietterich, T. G., Lathrop, R. H., & Lozano-Pérez, T. (1997) · Artificial Intelligence · doi:10.1016/s0004-3702(96)00034-3 · Cited by 2,866
Support Vector Machines for Multiple-Instance Learning
Andrews, S. J., Tsochantaridis, I., & Hofmann, T. (2002) · Cited by 1,404
Robust Fragments-based Tracking using the Integral Histogram
Adam, A., Rivlin, E., & Shimshoni, I. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (CVPR'06) · doi:10.1109/cvpr.2006.256 · Cited by 1,397
Online selection of discriminative tracking features
Collins, R. T., Liu, Y., & Leordeanu, M. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.205 · Cited by 1,208
Cited by
Online Object Tracking: A Benchmark
Wu, Y., Lim, J., & Yang, M.-H. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2013.312 · Cited by 4,138
Self-citationVisual object tracking using adaptive correlation filters
Bolme, D., Beveridge, J. R., Draper, B. A., & Lui, Y. M. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5539960 · Cited by 3,406
A Benchmark and Simulator for UAV Tracking
Mueller, M., Smith, N., & Ghanem, B. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46448-0_27 · Cited by 1,944
Struck: Structured output tracking with kernels
Hare, S., Saffari, A., & Torr, P. H. S. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126251 · Cited by 1,831
LaSOT: A High-Quality Benchmark for Large-Scale Single Object Tracking
Fan, H., Lin, L., Yang, F., Chu, P., Deng, G., Yu, S., Bai, H., Xu, Y., Liao, C., & Ling, H. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00552 · Cited by 1,742
“We evaluate 35 algorithms on LaSOT to provide extensive baselines, comprising deep trackers ( e.g ., MDNet [42], TRACA [5], CFNet [50], SiamFC [4], StructSiam [59], DSiam [16], SINT [49] and VITAL [48]), correlation fil-ter trackers with hand-crafted features ( e.g ., ECO HC [7], DSST [8], CN [11], CSK [19], KCF [20],…”
Background
Metadata
- Authors
- Babenko, B., Yang, M.-H., & Belongie, S.
- Venue
- 2009 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2009-06
- Pages
- 983-990
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2167089254
- Cited by
- 1,770
- References
- 24
- FWCI
- 82.60
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Babenko, B., Yang, M.-H., & Belongie, S. (2009). Visual tracking with online Multiple Instance Learning. 2009 IEEE Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2009.5206737
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
