Second-order polynomial models for background subtraction

Authors:
Alessandro Lanza;Federico Tombari;Luigi Di Stefano
Affiliations:
DEIS, University of Bologna, Bologna, Italy;DEIS, University of Bologna, Bologna, Italy;DEIS, University of Bologna, Bologna, Italy
Venue:
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
Year:
2010

Citing 6
Cited 0

Summed-area tables for texture mapping

SIGGRAPH '84 Proceedings of the 11th annual conference on Computer graphics and interactive techniques
A Texture-Based Method for Modeling the Background and Detecting Moving Objects

IEEE Transactions on Pattern Analysis and Machine Intelligence
An Intensity-augmented Ordinal Measure for Visual Correspondence

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Detecting Changes in Grey Level Sequences by ML Isotonic Regression

AVSS '06 Proceedings of the IEEE International Conference on Video and Signal Based Surveillance
The use of the area under the ROC curve in the evaluation of machine learning algorithms

Pattern Recognition
Accurate and Efficient Background Subtraction by Monotonic Second-Degree Polynomial Fitting

AVSS '10 Proceedings of the 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper is aimed at investigating background subtraction based on second-order polynomial models. Recently, preliminary results suggested that quadratic models hold the potential to yield superior performance in handling common disturbance factors, such as noise, sudden illumination changes and variations of camera parameters, with respect to state-of-the-art background subtraction methods. Therefore, based on the formalization of background subtraction as Bayesian regression of a second-order polynomial model, we propose here a thorough theoretical analysis aimed at identifying a family of suitable models and deriving the closed-form solutions of the associated regression problems. In addition, we present a detailed quantitative experimental evaluation aimed at comparing the different background subtraction algorithms resulting from theoretical analysis, so as to highlight those more favorable in terms of accuracy, speed and speed-accuracy tradeoff.