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Open AccessArticle

Unmanned Aerial Vehicle Object Tracking by Correlation Filter with Adaptive Appearance Model

School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an 710129, Shaanxi, China
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Sensors 2018, 18(9), 2751; https://doi.org/10.3390/s18092751
Received: 28 June 2018 / Revised: 13 August 2018 / Accepted: 13 August 2018 / Published: 21 August 2018
(This article belongs to the Special Issue Sensors Signal Processing and Visual Computing)
With the increasing availability of low-cost, commercially available unmanned aerial vehicles (UAVs), visual tracking using UAVs has become more and more important due to its many new applications, including automatic navigation, obstacle avoidance, traffic monitoring, search and rescue, etc. However, real-world aerial tracking poses many challenges due to platform motion and image instability, such as aspect ratio change, viewpoint change, fast motion, scale variation and so on. In this paper, an efficient object tracking method for UAV videos is proposed to tackle these challenges. We construct the fused features to capture the gradient information and color characteristics simultaneously. Furthermore, cellular automata is introduced to update the appearance template of target accurately and sparsely. In particular, a high confidence model updating strategy is developed according to the stability function. Systematic comparative evaluations performed on the popular UAV123 dataset show the efficiency of the proposed approach. View Full-Text
Keywords: UAV video; visual tracking; correlation filter; cellular automata; adaptive appearance model UAV video; visual tracking; correlation filter; cellular automata; adaptive appearance model
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Xue, X.; Li, Y.; Shen, Q. Unmanned Aerial Vehicle Object Tracking by Correlation Filter with Adaptive Appearance Model. Sensors 2018, 18, 2751.

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