Machine learning using template matching applied to object tracking in video data

David A. Zuehlke; Troy A. Henderson; Sonya A. H. McMullen

doi:10.1117/12.2518982

10 May 2019 Machine learning using template matching applied to object tracking in video data

David A. Zuehlke, Troy A. Henderson, Sonya A. H. McMullen

Proceedings Volume 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications; 110061S (2019) https://doi.org/10.1117/12.2518982
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

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.

Citation Download Citation

David A. Zuehlke, Troy A. Henderson, and Sonya A. H. McMullen "Machine learning using template matching applied to object tracking in video data", Proc. SPIE 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, 110061S (10 May 2019); https://doi.org/10.1117/12.2518982

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