2026-09-19T03:46:15Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1128672026-06-06T00:55:38Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Dina Katabi.
Farag, Emad William
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2017-12-20T18:15:24Z
2017-12-20T18:15:24Z
2016
2016
http://hdl.handle.net/1721.1/112867
1014334861
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 65-67).
This thesis presents a scalable real-time multi-object tracking system based on feature-less location measurements. The thesis introduces a two-stage object tracking algorithm along with a server infrastructure that allows users to view the tracking results live, replay old frames, or compute long-term analytics based on the tracking results. In the first tracking stage, consecutive measurements are connected to form short tracklets using an algorithm based on MHT. In the second stage, the tracklets are connected to form longer tracks in an algorithm that reduces the tracking problem to a minimum-cost flow problem. The system infrastructure allows for a large number of connected devices or sensors while reducing the possible points of failure. The tracking algorithms are evaluated in a controlled environment and in a daylong experiment in a real setting. In the latter, the number of people detected by the tracking algorithms was correct 83% of the time when tracking was done using noisy motion-based measurements.
by Emad William Farag.
M.Eng.
67 pages
eng
Massachusetts Institute of Technology
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Online multi-person tracking using feature-less location measurements
Thesis
application/pdf
Publication
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<Abstract>This thesis presents a scalable real-time multi-object tracking system based on feature-less location measurements. The thesis introduces a two-stage object tracking algorithm along with a server infrastructure that allows users to view the tracking results live, replay old frames, or compute long-term analytics based on the tracking results. In the first tracking stage, consecutive measurements are connected to form short tracklets using an algorithm based on MHT. In the second stage, the tracklets are connected to form longer tracks in an algorithm that reduces the tracking problem to a minimum-cost flow problem. The system infrastructure allows for a large number of connected devices or sensors while reducing the possible points of failure. The tracking algorithms are evaluated in a controlled environment and in a daylong experiment in a real setting. In the latter, the number of people detected by the tracking algorithms was correct 83% of the time when tracking was done using noisy motion-based measurements.</Abstract>
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