Multiple hypothesis object tracking for unsupervised self-learning: An ocean eddy tracking application

James H Faghmous, Muhammed Uluyol, Luke Styles, Matthew Le, Varun Mithal, Shyam Boriah, Vipin Kumar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

Abstract

Mesoscale ocean eddies transport heat, salt, energy, and nutrients across oceans. As a result, accurately identifying and tracking such phenomena are crucial for understanding ocean dynamics and marine ecosystem sustainability. Traditionally, ocean eddies are monitored through two phases: identification and tracking. A major challenge for such an approach is that the tracking phase is dependent on the performance of the identification scheme, which can be susceptible to noise and sampling errors. In this paper, we focus on tracking, and introduce the concept of multiple hypothesis assignment (MHA), which extends traditional multiple hypothesis tracking for cases where the features tracked are noisy or uncertain. Under this scheme, features are assigned to multiple potential tracks, and the final assignment is deferred until more data are available to make a relatively unambiguous decision. Unlike the most widely used methods in the eddy tracking literature, MHA uses contextual spatio-temporal information to take corrective measures autonomously on the detection step a posteriori and performs significantly better in the presence of noise. This study is also the first to empirically analyze the relative robustness of eddy tracking algorithms.

Original languageEnglish (US)
Title of host publicationProceedings of the 27th AAAI Conference on Artificial Intelligence, AAAI 2013
Pages1277-1283
Number of pages7
StatePublished - Dec 1 2013
Event27th AAAI Conference on Artificial Intelligence, AAAI 2013 - Bellevue, WA, United States
Duration: Jul 14 2013Jul 18 2013

Publication series

NameProceedings of the 27th AAAI Conference on Artificial Intelligence, AAAI 2013

Other

Other27th AAAI Conference on Artificial Intelligence, AAAI 2013
CountryUnited States
CityBellevue, WA
Period7/14/137/18/13

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  • Cite this

    Faghmous, J. H., Uluyol, M., Styles, L., Le, M., Mithal, V., Boriah, S., & Kumar, V. (2013). Multiple hypothesis object tracking for unsupervised self-learning: An ocean eddy tracking application. In Proceedings of the 27th AAAI Conference on Artificial Intelligence, AAAI 2013 (pp. 1277-1283). (Proceedings of the 27th AAAI Conference on Artificial Intelligence, AAAI 2013).