Date Available
6-3-2011
Year of Publication
2011
Degree Name
Master of Science in Electrical Engineering (MSEE)
Document Type
Thesis
College
Engineering
Department
Electrical Engineering
First Advisor
Dr.Sen-ching Samson Cheung
Abstract
Background subtraction is the technique of segmenting moving foreground objects from stationary or dynamic background scenes. Background subtraction is a critical step in many computer vision applications including video surveillance, tracking, gesture recognition etc. This thesis addresses the challenges associated with the background subtraction systems due to the sudden illumination changes happening in an indoor environment. Most of the existing techniques adapt to gradual illumination changes, but fail to cope with the sudden illumination changes. Here, we introduce a Global change reactive background subtraction to model these changes as a regression function of spatial image coordinates. The regression model is learned from highly probable background regions and the background model is compensated for the illumination changes by the model parameters estimated. Experiments were performed in the indoor environment to show the effectiveness of our approach in modeling the sudden illumination changes by a higher order regression polynomial. The results of non-linear SVM regression were also presented to show the robustness of our regression model.
Recommended Citation
Sathiyamoorthy, Edwin Premkumar, "GLOBAL CHANGE REACTIVE BACKGROUND SUBTRACTION" (2011). University of Kentucky Master's Theses. 86.
https://uknowledge.uky.edu/gradschool_theses/86
