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A novel object tracking method based on a mixture model

Research output: Contribution to journal › Article › peer-review

Standard

A novel object tracking method based on a mixture model. / Gao, Dongxu; Ju, Zhaojie; Cao, Jiangtao; Liu, Honghai.

In: International Journal of Intelligent Robotics and Applications, Vol. 2, No. 3, 09.2018, p. 361–371.

Research output: Contribution to journal › Article › peer-review

Harvard

Gao, D, Ju, Z, Cao, J & Liu, H 2018, 'A novel object tracking method based on a mixture model', International Journal of Intelligent Robotics and Applications, vol. 2, no. 3, pp. 361–371. https://doi.org/10.1007/s41315-018-0062-x

APA

Gao, D., Ju, Z., Cao, J., & Liu, H. (2018). A novel object tracking method based on a mixture model. International Journal of Intelligent Robotics and Applications, 2(3), 361–371. https://doi.org/10.1007/s41315-018-0062-x

Vancouver

Gao D, Ju Z, Cao J, Liu H. A novel object tracking method based on a mixture model. International Journal of Intelligent Robotics and Applications. 2018 Sep;2(3):361–371. https://doi.org/10.1007/s41315-018-0062-x

Author

Gao, Dongxu ; Ju, Zhaojie ; Cao, Jiangtao ; Liu, Honghai. / A novel object tracking method based on a mixture model. In: International Journal of Intelligent Robotics and Applications. 2018 ; Vol. 2, No. 3. pp. 361–371.

Bibtex

@article{818ec1f128944bdead5a1e54fbac55fa,
title = "A novel object tracking method based on a mixture model",
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.",
author = "Dongxu Gao and Zhaojie Ju and Jiangtao Cao and Honghai Liu",
year = "2018",
month = sep,
doi = "10.1007/s41315-018-0062-x",
language = "English",
volume = "2",
pages = "361–371",
journal = "International Journal of Intelligent Robotics and Applications",
issn = "2366-5971",
publisher = "Springer",
number = "3",

}

RIS

TY - JOUR

T1 - A novel object tracking method based on a mixture model

AU - Gao, Dongxu

AU - Ju, Zhaojie

AU - Cao, Jiangtao

AU - Liu, Honghai

PY - 2018/9

Y1 - 2018/9

N2 - 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.

AB - 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.

U2 - 10.1007/s41315-018-0062-x

DO - 10.1007/s41315-018-0062-x

M3 - Article

VL - 2

SP - 361

EP - 371

JO - International Journal of Intelligent Robotics and Applications

JF - International Journal of Intelligent Robotics and Applications

SN - 2366-5971

IS - 3

ER -

ID: 11527117