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Fast Visual Tracking via Dense Spatio-temporal Context Learning

  • Kaihua Zhang
  • Lei Zhang
  • Qingshan Liu
  • David Zhang
  • Ming-Hsuan Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8693)

Abstract

In this paper, we present a simple yet fast and robust algorithm which exploits the dense spatio-temporal context for visual tracking. Our approach formulates the spatio-temporal relationships between the object of interest and its locally dense contexts in a Bayesian framework, which models the statistical correlation between the simple low-level features (i.e., image intensity and position) from the target and its surrounding regions. The tracking problem is then posed by computing a confidence map which takes into account the prior information of the target location and thereby alleviates target location ambiguity effectively. We further propose a novel explicit scale adaptation scheme, which is able to deal with target scale variations efficiently and effectively. The Fast Fourier Transform (FFT) is adopted for fast learning and detection in this work, which only needs 4 FFT operations. Implemented in MATLAB without code optimization, the proposed tracker runs at 350 frames per second on an i7 machine. Extensive experimental results show that the proposed algorithm performs favorably against state-of-the-art methods in terms of efficiency, accuracy and robustness.

Keywords

Target Object Object Location Context Model Visual Tracking Multiple Instance Learn 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Kaihua Zhang
    • 1
  • Lei Zhang
    • 2
  • Qingshan Liu
    • 1
  • David Zhang
    • 2
  • Ming-Hsuan Yang
    • 3
  1. 1.S-mart GroupNanjing University of Information Science & TechnologyChina
  2. 2.Dept. of ComputingThe Hong Kong Polytechnic UniversityHongKong
  3. 3.Electrical Engineering and Computer ScienceUniversity of California at MercedUSA

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