Multi-target tracking via mixed integer optimization
Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2016.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2016
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| Online Access: | http://hdl.handle.net/1721.1/104998 |
| _version_ | 1826192346016907264 |
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| author | Saunders, Zachary Clayton |
| author2 | Sung-Hyun Son and Dimitris Bertsimas. |
| author_facet | Sung-Hyun Son and Dimitris Bertsimas. Saunders, Zachary Clayton |
| author_sort | Saunders, Zachary Clayton |
| collection | MIT |
| description | Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2016. |
| first_indexed | 2024-09-23T09:10:03Z |
| format | Thesis |
| id | mit-1721.1/104998 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T09:10:03Z |
| publishDate | 2016 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1049982019-04-10T21:08:58Z Multi-target tracking via mixed integer optimization MTT via MIO Saunders, Zachary Clayton Sung-Hyun Son and Dimitris Bertsimas. Massachusetts Institute of Technology. Operations Research Center. Massachusetts Institute of Technology. Operations Research Center. Operations Research Center. Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2016. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 85-87). Given a set of target detections over several time periods, this paper addresses the multi-target tracking problem (MTT) of optimally assigning detections to targets and estimating the trajectory of the targets over time. MTT has been studied in the literature via predominantly probabilistic methods. In contrast to these approaches, we propose the use of mixed integer optimization (MIO) models and local search algorithms that are (a) scalable, as they provide near optimal solutions for six targets and ten time periods in milliseconds to seconds, (b) general, as they make no assumptions on the data, (c) robust, as they can accommodate missed and false detections of the targets, and (d) easily implementable, as they use at most two tuning parameters. We evaluate the performance of the new methods using a novel metric for complexity of an instance and find that they provide high quality solutions both reliably and quickly for a large range of scenarios, resulting in a promising approach to the area of MTT. by Zachary Clayton Saunders. S.M. 2016-10-25T19:17:51Z 2016-10-25T19:17:51Z 2016 2016 Thesis http://hdl.handle.net/1721.1/104998 960810417 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 87 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Operations Research Center. Saunders, Zachary Clayton Multi-target tracking via mixed integer optimization |
| title | Multi-target tracking via mixed integer optimization |
| title_full | Multi-target tracking via mixed integer optimization |
| title_fullStr | Multi-target tracking via mixed integer optimization |
| title_full_unstemmed | Multi-target tracking via mixed integer optimization |
| title_short | Multi-target tracking via mixed integer optimization |
| title_sort | multi target tracking via mixed integer optimization |
| topic | Operations Research Center. |
| url | http://hdl.handle.net/1721.1/104998 |
| work_keys_str_mv | AT saunderszacharyclayton multitargettrackingviamixedintegeroptimization AT saunderszacharyclayton mttviamio |