The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Multi-task Correlation Particle Filter for Robust Object Tracking
Abstract
In this paper, we propose a multi-task correlation particle filter (MCPF) for robust visual tracking. We first present the multi-task correlation filter (MCF) that takes the interdependencies among different features into account to learn correlation filters jointly. The proposed MCPF is designed to exploit and complement the strength of a MCF and a particle filter. Compared with existing tracking methods based on correlation filters and particle filters, the proposed tracker has several advantages. First, it can shepherd the sampled particles toward the modes of the target state distribution via the MCF, thereby resulting in robust tracking performance. Second, it can effectively handle large-scale variation via a particle sampling strategy. Third, it can effectively maintain multiple modes in the posterior density using fewer particles than conventional particle filters, thereby lowering the computational cost. Extensive experimental results on three benchmark datasets demonstrate that the proposed MCPF performs favorably against the state-of-the-art methods.
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Citations
Cites
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
CONDENSATION—Conditional Density Propagation for Visual Tracking
Isard, M., & Blake, A. (1998) · International Journal of Computer Vision · doi:10.1023/a:1008078328650 · Cited by 4,886
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
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
Wu, Y., Lim, J., & Yang, M.-H. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2388226 · Cited by 3,371 · cited 9×
“Our method is evaluated on three benchmark datasets: OTB-2013 [36], OTB-2015 [37], and Temple Color [22].”
MethodInfluentialSelf-citationLearning 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
Visual tracking and recognition using appearance-adaptive models in particle filters
Zhou, S. K., Chellappa, R., & Moghaddam, B. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2004.836152 · Cited by 668 · cited 2×
“Consequently, numerous techniques have been presented to improve the sampling efficiency of particle filtering [19, 6, 20, 48].”
BackgroundMethod
Cited by
The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking
Du, D., Qi, Y., Yu, H., Yang, Y., Duan, K., Li, G., Zhang, W., Huang, Q., & Tian, Q. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01249-6_23 · Cited by 1,099
SiamCAR: Siamese Fully Convolutional Classification and Regression for Visual Tracking
Guo, D., Wang, J., Cui, Y., Wang, Z., & Chen, S. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00630 · Cited by 984
Learning Spatial-Temporal Regularized Correlation Filters for Visual Tracking
Li, F., Tian, C., Zuo, W., Zhang, L., & Yang, M.-H. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00515 · Cited by 935
Self-citationDeep visual tracking: Review and experimental comparison
Li, P., Wang, D., Wang, L., & Lu, H. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2017.11.007 · Cited by 517
Unveiling the Power of Deep Tracking
Bhat, G., Johnander, J., Danelljan, M., Khan, F. S., & Felsberg, M. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01216-8_30 · Cited by 514
Metadata
- Authors
- Zhang, T., Xu, C., & Yang, M.-H.
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 4819-4827
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2742165450
- Cited by
- 426
- References
- 8
- FWCI
- 21.92
- Type
- conference paper
- Language
- en
- Open access
- closed
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