Paper Info
Title
Background Subtraction in Video Using Recursive Mixture Models, Spatio-Temporal Filtering and Shadow Removal
Abstract
We describe our approach to segmenting moving objects from the color video data supplied by a nominally stationary camera. There are two main contributions in our work. The first contribution augments Zivkovic and Heijden's recursively updated Gaussian mixture model approach, with a multi-dimensional Gaussian kernel spatio-temporal smoothing transform. We show that this improves the segmentation performance of the original approach, particularly in adverse imaging conditions, such as when there is camera vibration. Our second contribution is to present a comprehensive comparative evaluation of shadow and highlight detection appoaches, which is an essential component of background subtraction in unconstrained outdoor scenes. A comparative evelaution of these approaches over different color-spaces is currently lacking in the literature. We show that both segmentation and shadow removal performs best when we use RGB color spaces.
Year
DOI
Venue
2009
10.1007/978-3-642-10520-3_109
ISVC
Keywords
Field
DocType
recursive mixture models,spatio-temporal filtering,camera vibration,original approach,recursively updated gaussian mixture,color video data,main contribution,comparative evelaution,rgb color space,background subtraction,model approach,shadow removal,comprehensive comparative evaluation,multi-dimensional gaussian kernel spatio-temporal,color space,mixture model,gaussian mixture model,gaussian kernel
Background subtraction,Computer vision,Shadow,Pattern recognition,Segmentation,Computer science,Filter (signal processing),Smoothing,Artificial intelligence,RGB color model,Gaussian function,Mixture model
Conference
Volume
ISSN
Citations 
5876
0302-9743
6
PageRank 
References 
Authors
0.51
14
4
Name
Order
Citations
PageRank
Zezhi Chen120415.92
Nick Pears241030.57
Michael Freeman3364.03
Jim Austin4116766.82