Online Multi-Object Tracking via Labeled Random Finite Set with Appearance Learning

Kim, D 2017, 'Online Multi-Object Tracking via Labeled Random Finite Set with Appearance Learning', in Proceedings of the International Conference on Control, Automation and Information Sciences (ICCAIS 2017), Chiang Mai, Thailand, 31 October - 3 November 2017, pp. 181-186.


Document type: Conference Paper
Collection: Conference Papers

Title Online Multi-Object Tracking via Labeled Random Finite Set with Appearance Learning
Author(s) Kim, D
Year 2017
Conference name ICCAIS 2017
Conference location Chiang Mai, Thailand
Conference dates 31 October - 3 November 2017
Proceedings title Proceedings of the International Conference on Control, Automation and Information Sciences (ICCAIS 2017)
Publisher IEEE
Place of publication United States
Start page 181
End page 186
Total pages 6
Abstract In this paper, a novel approach to online multi-object tracking is proposed via Labeled Random Finite Sets (RFS) combined with appearance learning. The Labeled RFS formulation of the multi-object state naturally accommodates a time-varying number of objects, track labels, and false positive rejection in a single Bayesian framework. The proposed algorithm exploits appearance feature information for the purpose of learning an object's appearance model, and uses this additional information in the construction an augmented likelihood which improves performance and facilitates track re-initialization. This approach enhances the baseline tracking algorithm and shows better performance with respect to mis-detections, occlusions and false track rejection. Competitive tracking results are shown compared to state-of-the-art algorithms on PETS benchmark [1] video datasets.
Subjects Signal Processing
DOI - identifier 10.1109/ICCAIS.2017.8217572
Copyright notice © 2017 Crown
ISBN 9781538631140
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