7 October 2016 Robust visual tracking via adaptive kernelized correlation filter
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Proceedings Volume 9902, Fourth International Conference on Wireless and Optical Communications; 99020P (2016) https://doi.org/10.1117/12.2261944
Event: Fourth International Conference on Wireless and Optical Communications, 2016, Beijing, China
Abstract
Correlation filter based trackers have proved to be very efficient and robust in object tracking with a notable performance competitive with state-of-art trackers. In this paper, we propose a novel object tracking method named Adaptive Kernelized Correlation Filter (AKCF) via incorporating Kernelized Correlation Filter (KCF) with Structured Output Support Vector Machines (SOSVM) learning method in a collaborative and adaptive way, which can effectively handle severe object appearance changes with low computational cost. AKCF works by dynamically adjusting the learning rate of KCF and reversely verifies the intermediate tracking result by adopting online SOSVM classifier. Meanwhile, we bring Color Names in this formulation to effectively boost the performance owing to its rich feature information encoded. Experimental results on several challenging benchmark datasets reveal that our approach outperforms numerous state-of-art trackers.
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Bo Wang, Desheng Wang, Qingmin Liao, "Robust visual tracking via adaptive kernelized correlation filter", Proc. SPIE 9902, Fourth International Conference on Wireless and Optical Communications, 99020P (7 October 2016); doi: 10.1117/12.2261944; https://doi.org/10.1117/12.2261944
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