Background model based on statistical local difference pattern

Authors:
Satoshi Yoshinaga;Atsushi Shimada;Hajime Nagahara;Rin-ichiro Taniguchi
Affiliations:
Kyushu University, Fukuoka, Japan;Kyushu University, Fukuoka, Japan;Kyushu University, Fukuoka, Japan;Kyushu University, Fukuoka, Japan
Venue:
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume Part I
Year:
2012

Citing 5
Cited 0

Robust object detection using a Radial Reach Filter (RRF)

Systems and Computers in Japan
A Texture-Based Method for Modeling the Background and Detecting Moving Objects

IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic Control of Adaptive Mixture-of-Gaussians Background Model

AVSS '06 Proceedings of the IEEE International Conference on Video and Signal Based Surveillance
Object detection based on a robust and accurate statistical multi-point-pair model

Pattern Recognition
Towards robust object detection: integrated background modeling based on spatio-temporal features

ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present a robust background model for object detection and report its evaluation results using the database of Background Models Challenge (BMC). Our background model is based on a statistical local feature. In particular, we use an illumination invariant local feature and describe its distribution by using a statistical framework. Thanks to the effectiveness of the local feature and the statistical framework, our method can adapt to both illumination and dynamic background changes. Experimental results, which are done thanks to the database of BMC, show that our method can detect foreground objects robustly against background changes.