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Proceedings Paper

Robust background modeling for enhancing object tracking in video
Author(s): Richard J. Wood; David Reed; Janet Lepanto; John M. Irvine
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Paper Abstract

Automated event recognition in video data has numerous practical applications. The ability to recognize events in practice depends on accurate tracking of objects in the video data. Scene complexity has a large effect on tracker performance. Background models can address this problem by providing a good estimate of the image region surrounding the object of interest. However, the utility of the background model depends on accurately representing current imaging conditions. Changing imaging conditions, such as lighting and weather, render the background model inaccurate, degrading the tracker performance. As a preprocessing step, developing a set of robust background models can substantially improve system performance. We present an approach to robustly modeling the background as a function of the data acquisition conditions. We will describe the formulation of these models and discuss model selection in the context of real-time processing. Using results from a recent experiment, we demonstrate empirically the performance benefits from using the robust background modeling.

Paper Details

Date Published: 19 June 2014
PDF: 9 pages
Proc. SPIE 9089, Geospatial InfoFusion and Video Analytics IV; and Motion Imagery for ISR and Situational Awareness II, 908902 (19 June 2014); doi: 10.1117/12.2047258
Show Author Affiliations
Richard J. Wood, Draper Lab. (United States)
David Reed, Draper Lab. (United States)
Janet Lepanto, Draper Lab. (United States)
John M. Irvine, Draper Lab. (United States)


Published in SPIE Proceedings Vol. 9089:
Geospatial InfoFusion and Video Analytics IV; and Motion Imagery for ISR and Situational Awareness II
Matthew F. Pellechia; Kannappan Palaniappan; Shiloh L. Dockstader; Paul B. Deignan; Peter J. Doucette; Donnie Self, Editor(s)

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