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Machine Vision and Applications

Erschienen in:

16.01.2019 | Original Paper

Hard negative mining for correlation filters in visual tracking

verfasst von: Zhuojin Sun, Yong Wang, Robert Laganière

Erschienen in: Machine Vision and Applications | Ausgabe 3/2019

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Abstract

Visual tracking is a fundamental computer vision task. Recent years have seen many tracking methods based on correlation filters exhibiting excellent performance. The strength of these methods comes from their ability to efficiently learn changes of the target appearance over time. A fundamental drawback to these methods is that the background of the object is not modeled over time which results in suboptimal results. In this paper, we propose a robust tracking method in which a hard negative mining scheme is employed in each frame. In addition, a target verification strategy is developed by introducing a peak signal-to-noise ratio (PSNR) criterion. The proposed method achieves strong tracking results, while maintaining a real-time speed of 30 frame per second without further optimization. Extensive experiments over multiple tracking datasets show the superior accuracy of our tracker compared to state-of-the-art methods including those based on deep learning features.

Vorheriger Artikel Deep domain adaptation with manifold aligned label transfer

Nächster Artikel Classification and comparison of on-line video summarisation methods

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Titel: Hard negative mining for correlation filters in visual tracking
verfasst von: Zhuojin Sun
Yong Wang
Robert Laganière
Publikationsdatum: 16.01.2019
Verlag: Springer Berlin Heidelberg
Erschienen in: Machine Vision and Applications / Ausgabe 3/2019
Print ISSN: 0932-8092
Elektronische ISSN: 1432-1769
DOI: https://doi.org/10.1007/s00138-019-01004-0

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