Robust Multi-Bernoulli Filtering for Visual Tracking

Kim, D and Jeon, M 2014, 'Robust Multi-Bernoulli Filtering for Visual Tracking', in Proceedings of the 2014 International Conference on Control, Automation and Information Sciences (ICCAIS 2014), Gwangju, South Korea, 2-5 December 2014, pp. 47-51.

Document type: Conference Paper
Collection: Conference Papers

Title Robust Multi-Bernoulli Filtering for Visual Tracking
Author(s) Kim, D
Jeon, M
Year 2014
Conference name ICCAIS 2014
Conference location Gwangju, South Korea
Conference dates 2-5 December 2014
Proceedings title Proceedings of the 2014 International Conference on Control, Automation and Information Sciences (ICCAIS 2014)
Publisher IEEE
Place of publication United States
Start page 47
End page 51
Total pages 5
Abstract To achieve reliable multi-object filtering in vision application, it is of great importance to determine appropriate model parameters. Parameters such as motion and measurement noise covariance can be chosen based on the image frame rate and the property of the designed detector. However, it is not trivial to obtain the average number of false positive measurements or detection probability due to the arbitrary visual scene characteristics from illumination condition or different fields of view. In this paper, we introduce the recently proposed robust multi-Bernoulli filter to deal with unknown clutter rate and detection profile in visual tracking applications. The robust multi-Bernoulli filter treats false positive responses as a special type of target so that the unknown clutter rate is estimated based on the estimated number of clutter targets. Performance evaluation with real videos demonstrates the effectiveness of the robust multi-Bernoulli filter and comparison results with the standard multi-object tracking algorithm show its reliability.
Subjects Computer Vision
Keyword(s) Random finite set
Multi-target tracking
Multi-Bernoulli filter
visual tracking
DOI - identifier 10.1109/ICCAIS.2014.7020566
Copyright notice © 2014 IEEE
ISBN 9781479972050
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Citation counts: TR Web of Science Citation Count  Cited 2 times in Thomson Reuters Web of Science Article | Citations
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