The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Accurate Scale Estimation for Robust Visual Tracking
Open accessAbstract
Robust scale estimation is a challenging problem in visual object tracking. Most existing methods fail to handle large scale variations in complex image sequences. This paper presents a novel approach for robust scale estimation in a tracking-by-detection framework. The proposed approach works by learning discriminative correlation filters based on a scale pyramid representation. We learn separate filters for translation and scale estimation, and show that this improves the performance compared to an exhaustive scale search. Our scale estimation approach is generic as it can be incorporated into any tracking method with no inherent scale estimation. Experiments are performed on 28 benchmark sequences with significant scale variations. Our results show that the proposed approach significantly improves the performance by 18.8 % in median distance precision compared to our baseline. Finally, we provide both quantitative and qualitative comparison of our approach with state-of-the-art trackers in literature. The proposed method is shown to outperform the best existing tracker by 16.6 % in median distance precision, while operating at real-time.
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Cites
Object Detection with Discriminatively Trained Part-Based Models
Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.167 · Cited by 9,853
High-Speed Tracking with Kernelized Correlation Filters
Henriques, J. F., Caseiro, R., Martins, P., & Batista, J. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2345390 · Cited by 5,753
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
Visual 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
Exploiting the Circulant Structure of Tracking-by-Detection with Kernels
Henriques, J. F., Caseiro, R., Martins, P., & Batista, J. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33765-9_50 · Cited by 2,310
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
Adaptive Color Attributes for Real-Time Visual Tracking
Danelljan, M., Khan, F. S., Felsberg, M., & Van De Weijer, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.143 · Cited by 1,500
Self-citationReal-Time Compressive Tracking
Zhang, K., Zhang, L., & Yang, M.-H. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33712-3_62 · Cited by 1,427
Visual tracking via adaptive structural local sparse appearance model
Jia, X., Lu, H., & Yang, M.-H. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6247880 · Cited by 1,188
P-N learning: Bootstrapping binary classifiers by structural constraints
Kalal, Z., Matas, J., & Mikolajczyk, K. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5540231 · Cited by 1,023
Cited by
Fully-Convolutional Siamese Networks for Object Tracking
Bertinetto, L., Valmadre, J., Henriques, J. F., Vedaldi, A., & Torr, P. H. S. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-48881-3_56 · Cited by 4,546
High Performance Visual Tracking with Siamese Region Proposal Network
Li, B., Yan, J., Wu, W., Zhu, Z., & Hu, X. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00935 · Cited by 3,023
Learning Multi-domain Convolutional Neural Networks for Visual Tracking
Nam, H., & Han, B. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.465 · Cited by 2,806
ECO: Efficient Convolution Operators for Tracking
Danelljan, M., Bhat, G., Khan, F. S., & Felsberg, M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.733 · Cited by 2,805
Self-citationA 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
Metadata
- Authors
- Danelljan, M., Häger, G., Shahbaz Khan, F., & Felsberg, M.
- Venue
- Proceedings of the British Machine Vision Conference 2014
- Publisher
- British Machine Vision Association
- Published
- 2014
- Pages
- 65.1-65.11
- OpenAlex
- W1997121481
- Cited by
- 2,087
- References
- 19
- FWCI
- 73.52
- Type
- conference paper
- Language
- en
- Open access
- green
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- Danelljan, M., Häger, G., Shahbaz Khan, F., & Felsberg, M. (2014). Accurate Scale Estimation for Robust Visual Tracking. Proceedings of the British Machine Vision Conference 2014. https://doi.org/10.5244/c.28.65
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