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Revisiting correlation-based filters for low-resolution and long-term visual tracking

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Abstract

In this paper, we revisit the problem of visual tracking by introducing a novel low-dimensional descriptor based on gradient distribution and specifically focus our attention on the problem of low-resolution and long-term visual tracking. We show that our tracking solution empowered by our proposed descriptor can effectively address the existing challenges in low-resolution and long-term visual tracking. Compared to the existing descriptors, the proposed method provides better robustness against local geometric and photometric variations. It adopts a new approach for aggregating information in a local neighborhood such that the sensitivity of the descriptor to noise and unreliable texture information is reduced. Integrating the proposed descriptor into a correlation-based tracking framework results in a robust and fast visual tracker. An extensive set of experiments on a number of large-scale benchmark datasets shows that the proposed method outperforms the state-of-the-art methods on low-resolution and long-term challenges, while achieving state-of-the-art performance in generic tracking.

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Notes

  1. The interested reader is referred to [10] for a more detailed derivation and description of the CN tracker.

  2. Shifting row and column elements of a matrix by m and n elements, respectively.

  3. For the sake of clarity, in all the figures only the results of the top ten trackers are included. Furthermore, the colors used in each of the plots indicate rankings in that figure (i.e., the red color is used for the best tracker/descriptor, green for the second best and so on).

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Authors and Affiliations

  1. Ferdowsi University of Mashhad, Mashhad, Iran

    Ehsan Fazl-Ersi & Masoud Kazemi Nooghabi

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  1. Ehsan Fazl-Ersi
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  2. Masoud Kazemi Nooghabi
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Correspondence to Ehsan Fazl-Ersi.

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Fazl-Ersi, E., Kazemi Nooghabi, M. Revisiting correlation-based filters for low-resolution and long-term visual tracking. Vis Comput 35, 1447–1459 (2019). https://doi.org/10.1007/s00371-018-1510-1

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