Paper Info
Title
Incorporating Object Tracking Feedback into Background Maintenance Framework
Abstract
Adaptive background modeling/subtraction techniques are popular, in particular, because they are able to cope with background variations that are due to lighting variations. Unfortunately these models also tend to adapt to foreground objects that become stationary for a period of time; as a result such objects are no longer considered for further processing. In this paper, we propose the first (to our knowledge) statistically consistent method for incorporating feedback from high-level motion model to modify adaptation behavior. Our approach is based on formulating the background maintenance problem as inference in a continuous state Hidden Markov Model, and combining it with a similarly formulated object tracker in a multichain graphical model framework. We demonstrate that the approximate filtering algorithm in such a framework outperforms the common feed-forward system while not imposing a significant extra computational burden.
Year
DOI
Venue
2005
10.1109/ACVMOT.2005.63
WACV/MOTION
Keywords
Field
DocType
high-level motion model,incorporating object tracking feedback,consistent method,continuous state,common feed-forward system,adaptation behavior,background maintenance problem,background variation,background maintenance framework,hidden markov model,multichain graphical model framework,adaptive background modeling,feed forward,hidden markov models,computer science,graphical models,object tracking,feedback,layout,artificial intelligence
Computer vision,Inference,Markov model,Computer science,Filter (signal processing),Video tracking,Artificial intelligence,Graphical model,Maintenance Problem,Hidden Markov model,Subtraction,Machine learning
Conference
Volume
ISBN
Citations 
2
0-7695-2271-8-2
2
PageRank 
References 
Authors
0.37
6
4
Name
Order
Citations
PageRank
Leonid Taycher124019.37
John W. Fisher III287874.44
Trevor Darrell3224131800.67
Fisher, J.W.4473.56