The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
Learning Collaborative Sparse Representation for Grayscale-Thermal Tracking
Abstract
Integrating multiple different yet complementary feature representations has been proved to be an effective way for boosting tracking performance. This paper investigates how to perform robust object tracking in challenging scenarios by adaptively incorporating information from grayscale and thermal videos, and proposes a novel collaborative algorithm for online tracking. In particular, an adaptive fusion scheme is proposed based on collaborative sparse representation in Bayesian filtering framework. We jointly optimize sparse codes and the reliable weights of different modalities in an online way. In addition, this paper contributes a comprehensive video benchmark, which includes 50 grayscale-thermal sequences and their ground truth annotations for tracking purpose. The videos are with high diversity and the annotations were finished by one single person to guarantee consistency. Extensive experiments against other state-of-the-art trackers with both grayscale and grayscale-thermal inputs demonstrate the effectiveness of the proposed tracking approach. Through analyzing quantitative results, we also provide basic insights and potential future research directions in grayscale-thermal tracking.
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Accurate Scale Estimation for Robust Visual Tracking
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Robust object tracking with online multiple instance learning
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Cited by
Image fusion meets deep learning: A survey and perspective
Zhang, H., Xu, H., Tian, X., Jiang, J., & Ma, J. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.06.008 · Cited by 834
RGB-T Salient Object Detection via Fusing Multi-Level CNN Features
Zhang, Q., Huang, N., Yao, L., Zhang, D., Shan, C., & Han, J. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2959253 · Cited by 247
Modality-correlation-aware sparse representation for RGB-infrared object tracking
Lan, X., Ye, M., Zhang, S., Zhou, H., & Yuen, P. C. (2020) · Pattern Recognition Letters · doi:10.1016/j.patrec.2018.10.002 · Cited by 121
Metadata
- Authors
- Li, C., Cheng, H., Hu, S., Liu, X., Tang, J., & Lin, L.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2016-12
- Volume
- 25
- Issue
- 12
- Pages
- 5743-5756
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W2527415613
- Cited by
- 391
- References
- 43
- FWCI
- 13.69
- Type
- journal article
- Language
- en
- Open access
- closed
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- Li, C., Cheng, H., Hu, S., Liu, X., Tang, J., & Lin, L. (2016). Learning Collaborative Sparse Representation for Grayscale-Thermal Tracking. IEEE Transactions on Image Processing. https://doi.org/10.1109/tip.2016.2614135
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