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Multiple video object tracking using variational inference

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Multiple video object tracking using variational inference. / Kangin, Dmitry; Kolev, Denis Georgiev; Markarian, Garegin.
2015 Sensor Data Fusion: Trends, Solutions, Applications (SDF 2015). IEEE, 2015. p. 47-52.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Kangin, D, Kolev, DG & Markarian, G 2015, Multiple video object tracking using variational inference. in 2015 Sensor Data Fusion: Trends, Solutions, Applications (SDF 2015). IEEE, pp. 47-52, Workshop on Sensor Data Fusion: Trends, Solutions, Applications, Bonn, Germany, 6/10/15. https://doi.org/10.1109/SDF.2015.7347702

APA

Kangin, D., Kolev, D. G., & Markarian, G. (2015). Multiple video object tracking using variational inference. In 2015 Sensor Data Fusion: Trends, Solutions, Applications (SDF 2015) (pp. 47-52). IEEE. https://doi.org/10.1109/SDF.2015.7347702

Vancouver

Kangin D, Kolev DG, Markarian G. Multiple video object tracking using variational inference. In 2015 Sensor Data Fusion: Trends, Solutions, Applications (SDF 2015). IEEE. 2015. p. 47-52 doi: 10.1109/SDF.2015.7347702

Author

Kangin, Dmitry ; Kolev, Denis Georgiev ; Markarian, Garegin. / Multiple video object tracking using variational inference. 2015 Sensor Data Fusion: Trends, Solutions, Applications (SDF 2015). IEEE, 2015. pp. 47-52

Bibtex

@inproceedings{f833683d8c4243478079b12b7c977c83,
title = "Multiple video object tracking using variational inference",
abstract = "In this article a Bayesian filter approximation is proposed for simultaneous multiple target detection and tracking and then applied for object detection on video from moving camera. The inference uses the evidence lower bound optimisation for Gaussian mixtures. The proposed filter is capable of real time data processing and may be used as a basis for data fusion. The method we propose was tested on the video with dynamic background,where the velocity with respect to the background is used to discriminate the objects. The framework does not depend on the feature space, that means that different feature spaces can be unrestrictedly used while preserving the structure of the filter.",
author = "Dmitry Kangin and Kolev, {Denis Georgiev} and Garegin Markarian",
year = "2015",
month = oct,
day = "6",
doi = "10.1109/SDF.2015.7347702",
language = "English",
isbn = "9781467371766",
pages = "47--52",
booktitle = "2015 Sensor Data Fusion: Trends, Solutions, Applications (SDF 2015)",
publisher = "IEEE",
note = "Workshop on Sensor Data Fusion: Trends, Solutions, Applications ; Conference date: 06-10-2015 Through 09-10-2015",

}

RIS

TY - GEN

T1 - Multiple video object tracking using variational inference

AU - Kangin, Dmitry

AU - Kolev, Denis Georgiev

AU - Markarian, Garegin

PY - 2015/10/6

Y1 - 2015/10/6

N2 - In this article a Bayesian filter approximation is proposed for simultaneous multiple target detection and tracking and then applied for object detection on video from moving camera. The inference uses the evidence lower bound optimisation for Gaussian mixtures. The proposed filter is capable of real time data processing and may be used as a basis for data fusion. The method we propose was tested on the video with dynamic background,where the velocity with respect to the background is used to discriminate the objects. The framework does not depend on the feature space, that means that different feature spaces can be unrestrictedly used while preserving the structure of the filter.

AB - In this article a Bayesian filter approximation is proposed for simultaneous multiple target detection and tracking and then applied for object detection on video from moving camera. The inference uses the evidence lower bound optimisation for Gaussian mixtures. The proposed filter is capable of real time data processing and may be used as a basis for data fusion. The method we propose was tested on the video with dynamic background,where the velocity with respect to the background is used to discriminate the objects. The framework does not depend on the feature space, that means that different feature spaces can be unrestrictedly used while preserving the structure of the filter.

U2 - 10.1109/SDF.2015.7347702

DO - 10.1109/SDF.2015.7347702

M3 - Conference contribution/Paper

SN - 9781467371766

SP - 47

EP - 52

BT - 2015 Sensor Data Fusion: Trends, Solutions, Applications (SDF 2015)

PB - IEEE

T2 - Workshop on Sensor Data Fusion: Trends, Solutions, Applications

Y2 - 6 October 2015 through 9 October 2015

ER -