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An Efficient Visual Tracking Method for Multiple Moving Targets

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

Published

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An Efficient Visual Tracking Method for Multiple Moving Targets. / Chen, Xiaohui; Bull, David R.; Mihaylova, Lyudmila; Canagarajah, Nishan.

Computational Intelligence and Security Workshops, 2007. CISW 2007. International Conference on. Washington, DC, USA : IEEE Computer Society, 2007. p. 267-270.

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

Harvard

Chen, X, Bull, DR, Mihaylova, L & Canagarajah, N 2007, An Efficient Visual Tracking Method for Multiple Moving Targets. in Computational Intelligence and Security Workshops, 2007. CISW 2007. International Conference on. IEEE Computer Society, Washington, DC, USA, pp. 267-270. https://doi.org/10.1109/CISW.2007.4425488

APA

Chen, X., Bull, D. R., Mihaylova, L., & Canagarajah, N. (2007). An Efficient Visual Tracking Method for Multiple Moving Targets. In Computational Intelligence and Security Workshops, 2007. CISW 2007. International Conference on (pp. 267-270). IEEE Computer Society. https://doi.org/10.1109/CISW.2007.4425488

Vancouver

Chen X, Bull DR, Mihaylova L, Canagarajah N. An Efficient Visual Tracking Method for Multiple Moving Targets. In Computational Intelligence and Security Workshops, 2007. CISW 2007. International Conference on. Washington, DC, USA: IEEE Computer Society. 2007. p. 267-270 https://doi.org/10.1109/CISW.2007.4425488

Author

Chen, Xiaohui ; Bull, David R. ; Mihaylova, Lyudmila ; Canagarajah, Nishan. / An Efficient Visual Tracking Method for Multiple Moving Targets. Computational Intelligence and Security Workshops, 2007. CISW 2007. International Conference on. Washington, DC, USA : IEEE Computer Society, 2007. pp. 267-270

Bibtex

@inproceedings{03518d7a849e41e2a85136a3067d9eb0,
title = "An Efficient Visual Tracking Method for Multiple Moving Targets",
abstract = "An efficient algorithm of the edge detection according to integrating the edge gradient with the average filter is proposed, which can significantly reduce sensitivity of the background subtraction method to noise and illumination. Taking into account the features of the target such as colour, size, etc., a new modified nearest neighbour (NN) algorithm for data association using the target features is designed. A designed interacting multiple model (IMM) filter is utilized to track the maneuvering target motion, i.e. the feature point (called the centroid of the target) motion of the target. The algorithms are validated via an example with natural video sequences. The results show the algorithms are performances and validity for visual tracking. In complex environment, the algorithm can still work well.",
author = "Xiaohui Chen and Bull, {David R.} and Lyudmila Mihaylova and Nishan Canagarajah",
year = "2007",
doi = "10.1109/CISW.2007.4425488",
language = "English",
isbn = "978-0-7695-3073-4",
pages = "267--270",
booktitle = "Computational Intelligence and Security Workshops, 2007. CISW 2007. International Conference on",
publisher = "IEEE Computer Society",

}

RIS

TY - GEN

T1 - An Efficient Visual Tracking Method for Multiple Moving Targets

AU - Chen, Xiaohui

AU - Bull, David R.

AU - Mihaylova, Lyudmila

AU - Canagarajah, Nishan

PY - 2007

Y1 - 2007

N2 - An efficient algorithm of the edge detection according to integrating the edge gradient with the average filter is proposed, which can significantly reduce sensitivity of the background subtraction method to noise and illumination. Taking into account the features of the target such as colour, size, etc., a new modified nearest neighbour (NN) algorithm for data association using the target features is designed. A designed interacting multiple model (IMM) filter is utilized to track the maneuvering target motion, i.e. the feature point (called the centroid of the target) motion of the target. The algorithms are validated via an example with natural video sequences. The results show the algorithms are performances and validity for visual tracking. In complex environment, the algorithm can still work well.

AB - An efficient algorithm of the edge detection according to integrating the edge gradient with the average filter is proposed, which can significantly reduce sensitivity of the background subtraction method to noise and illumination. Taking into account the features of the target such as colour, size, etc., a new modified nearest neighbour (NN) algorithm for data association using the target features is designed. A designed interacting multiple model (IMM) filter is utilized to track the maneuvering target motion, i.e. the feature point (called the centroid of the target) motion of the target. The algorithms are validated via an example with natural video sequences. The results show the algorithms are performances and validity for visual tracking. In complex environment, the algorithm can still work well.

U2 - 10.1109/CISW.2007.4425488

DO - 10.1109/CISW.2007.4425488

M3 - Conference contribution/Paper

SN - 978-0-7695-3073-4

SP - 267

EP - 270

BT - Computational Intelligence and Security Workshops, 2007. CISW 2007. International Conference on

PB - IEEE Computer Society

CY - Washington, DC, USA

ER -