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Machine learning using template matching applied to object tracking in video data
Author(s): David A. Zuehlke; Troy A. Henderson; Sonya A. H. McMullen
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Paper Abstract

This paper presents the algorithms for tracking a moving object through video data using template matching. As the object translates and rotates, the template is adaptively updated so that the object is never lost while in frame. The algorithms were developed in MATLAB and applied to a video of a quadcopter in flight in both visible and infrared imagery. The normalized cross-correlation algorithm is the core of the research, providing an invariant of scale method to perform the template match. Then a bounding box is applied to the matched area and center of mass centroiding allow the object to be tracked frame-to-frame.

Paper Details

Date Published: 10 May 2019
PDF: 7 pages
Proc. SPIE 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, 110061S (10 May 2019); doi: 10.1117/12.2518982
Show Author Affiliations
David A. Zuehlke, Embry-Riddle Aeronautical Univ. (United States)
Troy A. Henderson, Embry-Riddle Aeronautical Univ. (United States)
Sonya A. H. McMullen, T2S Solutions LLC (United States)


Published in SPIE Proceedings Vol. 11006:
Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications
Tien Pham, Editor(s)

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