Object tracking in the presence of shaking motions

  • Manna Dai
  • Shuying Cheng
  • Xiangjian He
  • Dadong Wang
Original Article


Visual tracking can be particularly interpreted as a process of searching for targets and optimizing the searching. In this paper, we present a novel tracker framework for tracking shaking targets. We formulate the underlying geometrical relevance between a search scope and a target displacement. A uniform sampling among the search scopes is implemented by sliding windows. To alleviate any possible redundant matching, we propose a double-template structure comprising of initial and previous tracking results. The element-wise similarities between a template and its candidates are calculated by jointly using kernel functions which provide a better outlier rejection property. The STC algorithm is used to improve the tracking results by maximizing a confidence map incorporating temporal and spatial context cues about the tracked targets. For better adaptation to appearance variations, we employ a linear interpolation to update the context prior probability of the STC method. Both qualitative and quantitative evaluations are performed on all sequences that contain shaking motions and are selected from the OTB-50 challenging benchmark. The proposed approach is compared with and outperforms 12 state-of-the-art tracking methods on the selected sequences while running on MATLAB without code optimization. We have also performed further experiments on the whole OTB-50 and VOT 2015 datasets. Although the most of sequences in these two datasets do not contain motion blur that this paper is focusing on, the results of our method are still favorable compared with all of the state-of-the-art approaches.


Shaking targets Uniform sampling Kernel Temporal and spatial context 



This work was supported by Fujian Provincial Department of Science and Technology (Grant No. 2015H0021).

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.


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Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  • Manna Dai
    • 1
    • 2
    • 3
  • Shuying Cheng
    • 1
    • 4
  • Xiangjian He
    • 2
  • Dadong Wang
    • 3
  1. 1.Institute of Micro/Nano Devices and Solar Cells, College of Physics and Information EngineeringFuzhou UniversityFuzhouChina
  2. 2.University of Technology SydneySydneyAustralia
  3. 3.Commonwealth Scientific and Industrial Research Organisation (CSIRO)SydneyAustralia
  4. 4.Jiangsu Collaborative Innovation Center of Photovolatic Science and EngineeringChangzhouChina

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