A novelty detection approach for foreground region detection in videos with quasi-stationary backgrounds

Authors:
Alireza Tavakkoli;Mircea Nicolescu;George Bebis
Affiliations:
Computer Vision Lab., Department of Computer Science and Engineering, University of Nevada, Reno;Computer Vision Lab., Department of Computer Science and Engineering, University of Nevada, Reno;Computer Vision Lab., Department of Computer Science and Engineering, University of Nevada, Reno
Venue:
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I
Year:
2006

Citing 10
Cited 1

Pfinder: Real-Time Tracking of the Human Body

IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Patterns of Activity Using Real-Time Tracking

IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Independent Motion: The Statistics of Temporal Continuity

IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Tracking Using Adaptive Color Mixture Models

ACCV '98 Proceedings of the Third Asian Conference on Computer Vision-Volume I - Volume I
Support Vector Data Description

Machine Learning
Effective Gaussian Mixture Learning for Video Background Subtraction

IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion-based background subtraction using adaptive kernel density estimation

CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Image segmentation in video sequences: a probabilistic approach

UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Background updating for visual surveillance

ISVC'05 Proceedings of the First international conference on Advances in Visual Computing
Automatic robust background modeling using multivariate non-parametric kernel density estimation for visual surveillance

ISVC'05 Proceedings of the First international conference on Advances in Visual Computing

A genetic approach to training support vector data descriptors for background modeling in video data

ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

Detecting regions of interest in video sequences is one of the most important tasks in many high level video processing applications. In this paper a novel approach based on support vector data description is presented, which detects foreground regions in videos with quasi-stationary backgrounds. The main contribution of this paper is the novelty detection approach which automatically segments video frames into background/foreground regions. By using support vector data description for each pixel, the decision boundary for the background class is modeled without the need to statistically model its probability density function. The proposed method is able to achieve very accurate foreground region detection rates even in very low contrast video sequences, and in the presence of quasi-stationary backgrounds. As opposed to many statistical background modeling approaches, the only critical parameter that needs to be adjusted in our method is the number of background training frames.