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|Title:||Part-based background-aware tracking for UAV with convolutional features||Authors:||Fu, CH
|Keywords:||Visual object tracking
Unmanned aerial vehicle (UAV)
Convolutional neural network
Background-aware correlation filter
Gaussian process regression
|Issue Date:||2019||Publisher:||Institute of Electrical and Electronics Engineers||Source:||IEEE access, 2019, v. 7, p. 79997-80010 How to cite?||Journal:||IEEE access||Abstract:||In recent years, visual tracking is a challenging task in UAV applications. The standard correlation filter (CF) has been extensively applied for UAV object tracking. However, the CF-based tracker severely suffers from boundary effects and cannot effectively cope with object occlusion, which results in suboptimal performance. Besides, it is still a tough task to obtain an appearance model precisely with hand-crafted features. In this paper, a novel part-based tracker is proposed for the UAV. With successive cropping operations, the tracking object is separated into several parts. More specially, the background-aware correlation filters with different cropping matrices are applied. To estimate the translation and scale variation of the tracking object, a structured comparison, and a Bayesian inference approach are proposed, which jointly achieve a coarse-to-fine strategy. Moreover, an adaptive mechanism is used to update the local appearance model of each part with a Gaussian process regression method. To construct a better appearance model, features extracted from the convolutional neural network are utilized instead of hand-crafted features. Through extensive experiments, the proposed tracker reaches competitive performance on 123 challenging UAV image sequences and outperforms other 20 popular state-of-the-art visual trackers in terms of overall performance and different challenging attributes.||URI:||http://hdl.handle.net/10397/81300||ISSN:||2169-3536||DOI:||10.1109/ACCESS.2019.2922703||Rights:||© 2019 IEEE. Translations and content mining are permitted for academic research only.
Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
The following publication C. Fu, Y. Zhang, Z. Huang, R. Duan and Z. Xie, "Part-Based Background-Aware Tracking for UAV With Convolutional Features," in IEEE Access, vol. 7, pp. 79997-80010, 2019 is available at https://dx.doi.org/10.1109/ACCESS.2019.2922703
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Citations as of Oct 15, 2019
Citations as of Oct 15, 2019
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