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Learning Temporal Context for Correlation Tracking with Scale Estimation

  • Yuhao CuiEmail author
  • Haoqian Wang
  • Xingzheng Wang
  • Yi Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10735)

Abstract

Visual object tracking is a fundamental task in computer vision with its wide range of applications. In this paper, we propose a robust algorithm based on the kernelized correlation filter framework to handle occlusions or scale variations. Our algorithm takes into account the relationships between the target object and its surrounding context, and learns a discriminative correlation filter for the estimation of the new position. Another discriminative regression model via constructing the target pyramid is introduced to estimate the optimal scale. The proposed algorithm integrated with two discriminative regression models can track complex targets with occlusion and deformation at real-time. The competitive experimental results on the dataset sequences show that the proposed tracker outperforms other state-of-the-art methods, in both the precision and the success rate.

Keywords

Object tracking Correlation filter Temporal context Scale pyramid Fast Fourier Transform (FFT) 

Notes

Acknowledgements

This work is partially supported by the NSFC fund (61571259, 61531014, 61471213), Shenzhen Fundamental Research fund (JCYJ20170307153051701), Shenzhen Public Technology Platform fund (GGFW2017040714161462).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Yuhao Cui
    • 1
    Email author
  • Haoqian Wang
    • 1
    • 3
  • Xingzheng Wang
    • 1
  • Yi Yang
    • 2
  1. 1.Key Laboratory of Broadband Network and Multimedia, Graduate School at ShenzhenTsinghua UniversityShenzhenChina
  2. 2.LUSTER LightTech Co., Ltd.BeijingChina
  3. 3.Shenzhen Institute of Future Media TechnologyShenzhenChina

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