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The Visual Computer

, Volume 35, Issue 3, pp 371–384 | Cite as

Objectness-based smoothing stochastic sampling and coherence approximate nearest neighbor for visual tracking

  • Jimmy T. MbelwaEmail author
  • Qingjie Zhao
  • Yao Lu
  • Hao Liu
  • Fasheng Wang
  • Mercy Mbise
Original Article
  • 187 Downloads

Abstract

In visual tracking, most of the tracking methods suffer from abrupt motions. To address this problem, we propose a novel method for tracking abrupt motions using objectness embedded in smoothing stochastic sampling and improved Tree coherency approximate nearest neighbor. An improved coherence approximate nearest neighbor is utilized to estimate the promising regions as prior knowledge. Moreover, objectness is employed as an objectness proposal function for handling dynamic motions. Finally, both prior knowledge and objectness proposal are integrated into the smoothing stochastic approximate Monte Carlo to predict a new state of the target object. Experimental comparison with other tracking methods and proposed method was carried on some of the challenging video sequences. Experimental results demonstrate that our proposed method outperforms other state-of-the-art tracking methods for dealing with abrupt motions in terms of effectiveness and robustness.

Keywords

Abrupt motion tracking Coherence approximate nearest neighbor Objectness Stochastic Approximate 

Notes

Acknowledgements

This work was supported by Chinese Government Scholarship under China Scholarship Council (CSC), National Natural Science Foundation of China (Grant No. 61175096, NSFC No. 61300082), Liaoning Natural Science Foundation (No. 2015020015) and 2016 project funded by China Postdoctoral Science Foundation (No. 2016M601306).

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Jimmy T. Mbelwa
    • 1
    Email author
  • Qingjie Zhao
    • 1
  • Yao Lu
    • 1
  • Hao Liu
    • 1
  • Fasheng Wang
    • 2
  • Mercy Mbise
    • 3
  1. 1.Beijing Laboratory of Intelligent Information Technology, School of Computer ScienceBeijing Institute of TechnologyHaidian District, BeijingChina
  2. 2.School of Information and Communication EngineeringDalian Minzu UniversityJinzhou New District, DalianChina
  3. 3.College of Information and Communication TechnologiesUniversity of Dar es salaamDar es salaamTanzania

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