A Generalized Labeled Multi-Bernoulli Tracker for Time Lapse Cell Migration

Kim, D, Vo, B, Thian, A and Choi, Y 2017, 'A Generalized Labeled Multi-Bernoulli Tracker for Time Lapse Cell Migration', in Proceedings of the International Conference on Control, Automation and Information Sciences (ICCAIS 2017), Chiang Mai, Thailand, 31 October - 3 November 2017, pp. 20-25.


Document type: Conference Paper
Collection: Conference Papers

Title A Generalized Labeled Multi-Bernoulli Tracker for Time Lapse Cell Migration
Author(s) Kim, D
Vo, B
Thian, A
Choi, Y
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 20
End page 25
Total pages 6
Abstract Tracking is a means to accomplish the more fundamental task of extracting relevant information about cell behavior from time-lapse microscopy data. Hence, characterizing uncertainty or confidence in the information inferred from the data is as important as the tracking of the cells. In this paper, we show that in addition to being a principled Bayesian multi-object tracking approach, the Random Finite Set (RFS) framework is capable of providing consistent characterization of uncertainty for the information inferred from the data. In particular, we use an efficient implementation of the Generalized Labeled Multi-Bernoulli (GLMB) filter to track a large number of cells in a cell migration experiment and demonstrate how to characterize uncertainty on variables inferred from the data such as cell counts, survival rate, birth rate, mean position, mean velocity using standard constructs from RFS theory.
Subjects Signal Processing
Keyword(s) Cell tracking
Bayesian filtering
Random finite set
DOI - identifier 10.1109/ICCAIS.2017.8217576
Copyright notice © 2017 Crown
ISBN 9781538631140
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