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A novel object tracking method based on a mixture model

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Abstract

Object tracking has been applied in many fields such as intelligent surveillance and computer vision. Although much progress has been made, there are still many puzzles which pose a huge challenge to object tracking. Currently, the problems are mainly caused by occlusion, similar object appearance and background clutters. A novel method based on a mixture model was proposed for solving these issues. The mixture model was integrated into a Bayes framework with the combination of locally dense contexts feature and the fundamental image information (i.e. the relationship between the object and its surrounding regions). This is because that the tracking problem can be seen as a prediction question, which can be solved using the Bayes method. In addition, both scale variations and templet updating are considered to assure the effectiveness of the proposed algorithm. Furthermore, the Fourier Transform (FT) is used when solving the Bayes equation to make the algorithm run in a real-time system. Therefore, the MMOT (Mixture model for object tracking) can run faster and perform better than existing algorithms on some challenging images sequences in terms of accuracy, quickness and robustness.

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Acknowledgements

The authors would like to acknowledge the support from the EU Seventh Framework Programme (FP7)-ICT under Grant no. 611391, Natural Science Foundation of China under Grant no. 51575412, 51575338 and 51575407, China Scholarship Council (Grant no. 201508060340) and Research Project of State Key Lab of Digital Manufacturing Equipment & Technology of China under Grant no. DMETKF2017003.

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Authors and Affiliations

  1. School of Computing, University of Portsmouth, Portsmouth, UK

    Dongxu Gao, Zhaojie Ju & Honghai Liu

  2. Faculty of Information and Control Engineering, Liaoning Shihua University, Fushun, China

    Jiangtao Cao

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  1. Dongxu Gao
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  2. Zhaojie Ju
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  3. Jiangtao Cao
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  4. Honghai Liu
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Correspondence to Zhaojie Ju.

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Gao, D., Ju, Z., Cao, J. et al. A novel object tracking method based on a mixture model. Int J Intell Robot Appl 2, 361–371 (2018). https://doi.org/10.1007/s41315-018-0062-x

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