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Data fusion for unsupervised video object detection, tracking and geo-positioning

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Published

Standard

Data fusion for unsupervised video object detection, tracking and geo-positioning. / Kolev, Denis Georgiev; Markarian, Garegin; Kangin, Dmitry.

Information Fusion (Fusion), 2015 18th International Conference on. IEEE, 2015. p. 142-149.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Kolev, DG, Markarian, G & Kangin, D 2015, Data fusion for unsupervised video object detection, tracking and geo-positioning. in Information Fusion (Fusion), 2015 18th International Conference on. IEEE, pp. 142-149, International Conference on Information Fusion'2015, Washington DC USA, United States, 4/07/15. <http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7266555>

APA

Kolev, D. G., Markarian, G., & Kangin, D. (2015). Data fusion for unsupervised video object detection, tracking and geo-positioning. In Information Fusion (Fusion), 2015 18th International Conference on (pp. 142-149). IEEE. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7266555

Vancouver

Kolev DG, Markarian G, Kangin D. Data fusion for unsupervised video object detection, tracking and geo-positioning. In Information Fusion (Fusion), 2015 18th International Conference on. IEEE. 2015. p. 142-149

Author

Kolev, Denis Georgiev ; Markarian, Garegin ; Kangin, Dmitry. / Data fusion for unsupervised video object detection, tracking and geo-positioning. Information Fusion (Fusion), 2015 18th International Conference on. IEEE, 2015. pp. 142-149

Bibtex

@inproceedings{325ab46fbd8f4fee9d22f8bfa8119e37,
title = "Data fusion for unsupervised video object detection, tracking and geo-positioning",
abstract = "In this work we describe a system and propose a novel algorithm for moving object detection and tracking based on video feed. Apart of many well-known algorithms, it performs detection in unsupervised style, using velocity criteria for the objects detection. The algorithm utilises data from a single camera and Inertial Measurement Unit (IMU) sensors and performs fusion of video and sensory data captured from the UAV. The algorithm includes object tracking and detection, augmented by object geographical co-ordinates estimation. The algorithm can be generalised for any particular video sensor and is not restricted to any specific applications. For object tracking, Bayesian filter scheme combined with approximate inference is utilised. Object localisation in real-world co-ordinates is based on the tracking results and IMU sensor measurements.",
keywords = "Bayesian filters, UAV, object tracking, unsupervised detection, rigid motion segmentation",
author = "Kolev, {Denis Georgiev} and Garegin Markarian and Dmitry Kangin",
year = "2015",
month = jul,
day = "4",
language = "English",
isbn = "9781479974047",
pages = "142--149",
booktitle = "Information Fusion (Fusion), 2015 18th International Conference on",
publisher = "IEEE",
note = "International Conference on Information Fusion'2015 ; Conference date: 04-07-2015 Through 08-07-2015",

}

RIS

TY - GEN

T1 - Data fusion for unsupervised video object detection, tracking and geo-positioning

AU - Kolev, Denis Georgiev

AU - Markarian, Garegin

AU - Kangin, Dmitry

PY - 2015/7/4

Y1 - 2015/7/4

N2 - In this work we describe a system and propose a novel algorithm for moving object detection and tracking based on video feed. Apart of many well-known algorithms, it performs detection in unsupervised style, using velocity criteria for the objects detection. The algorithm utilises data from a single camera and Inertial Measurement Unit (IMU) sensors and performs fusion of video and sensory data captured from the UAV. The algorithm includes object tracking and detection, augmented by object geographical co-ordinates estimation. The algorithm can be generalised for any particular video sensor and is not restricted to any specific applications. For object tracking, Bayesian filter scheme combined with approximate inference is utilised. Object localisation in real-world co-ordinates is based on the tracking results and IMU sensor measurements.

AB - In this work we describe a system and propose a novel algorithm for moving object detection and tracking based on video feed. Apart of many well-known algorithms, it performs detection in unsupervised style, using velocity criteria for the objects detection. The algorithm utilises data from a single camera and Inertial Measurement Unit (IMU) sensors and performs fusion of video and sensory data captured from the UAV. The algorithm includes object tracking and detection, augmented by object geographical co-ordinates estimation. The algorithm can be generalised for any particular video sensor and is not restricted to any specific applications. For object tracking, Bayesian filter scheme combined with approximate inference is utilised. Object localisation in real-world co-ordinates is based on the tracking results and IMU sensor measurements.

KW - Bayesian filters

KW - UAV

KW - object tracking

KW - unsupervised detection

KW - rigid motion segmentation

M3 - Conference contribution/Paper

SN - 9781479974047

SP - 142

EP - 149

BT - Information Fusion (Fusion), 2015 18th International Conference on

PB - IEEE

T2 - International Conference on Information Fusion'2015

Y2 - 4 July 2015 through 8 July 2015

ER -