Multi-Bernoulli Filtering for Keypoint-based Visual Tracking

Kim, D 2016, 'Multi-Bernoulli Filtering for Keypoint-based Visual Tracking', in Proceedings of the International Conference on Control, Automation and Information Sciences (ICCAIS 2016), Ansan, South Korea, 27-29 October 2016, pp. 37-41.


Document type: Conference Paper
Collection: Conference Papers

Title Multi-Bernoulli Filtering for Keypoint-based Visual Tracking
Author(s) Kim, D
Year 2016
Conference name ICCAIS 2016
Conference location Ansan, South Korea
Conference dates 27-29 October 2016
Proceedings title Proceedings of the International Conference on Control, Automation and Information Sciences (ICCAIS 2016)
Publisher IEEE
Place of publication United States
Start page 37
End page 41
Total pages 5
Abstract In this paper, we consider a single object visual tracking problem using multi-object filtering technique. We represent object appearance as a multi-object distribution of keypoints. Hidden positions of keypoints are observed by using SURF feature detectors and multi-Bernoulli filtering is used for tracking of keypoints. Unlike other feature matching based object trackers, multi-Bernoulli filtering based tracker is free from combinatorial matching problem. The estimated number of keypoints can be used as a quality measure to determine track re-initialization when it is necessary. Experimental results show that multi-object filtering can be one of effective solutions for single object visual tracking.
Subjects Computer Vision
DOI - identifier 10.1109/ICCAIS.2016.7822432
Copyright notice © 2016 Crown
ISBN 9781509006502
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