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

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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.

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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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Authors and Affiliations

  1. Beijing Laboratory of Intelligent Information Technology, School of Computer Science, Beijing Institute of Technology, Haidian District, Beijing, 100081, China

    Jimmy T. Mbelwa, Qingjie Zhao, Yao Lu & Hao Liu

  2. School of Information and Communication Engineering, Dalian Minzu University, Jinzhou New District, Dalian, 116600, China

    Fasheng Wang

  3. College of Information and Communication Technologies, University of Dar es salaam, P.O. Box 35062, Dar es salaam, Tanzania

    Mercy Mbise

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  1. Jimmy T. Mbelwa
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  2. Qingjie Zhao
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  3. Yao Lu
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  4. Hao Liu
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  5. Fasheng Wang
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  6. Mercy Mbise
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Correspondence to Jimmy T. Mbelwa.

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Mbelwa, J.T., Zhao, Q., Lu, Y. et al. Objectness-based smoothing stochastic sampling and coherence approximate nearest neighbor for visual tracking. Vis Comput 35, 371–384 (2019). https://doi.org/10.1007/s00371-018-1470-5

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