Paper Info
Title
A Framework for High-Level Feedback to Adaptive, Per-Pixel, Mixture-of-Gaussian Background Models
Abstract
Time-Adaptive, Per-Pixel Mixtures Of Gaussians (TAPPMOGs) have recently become a popular choice for robust modeling and removal of complex and changing backgrounds at the pixel level. However, TAPPMOG-based methods cannot easily be made to model dynamic backgrounds with highly complex appearance, or to adapt promptly to sudden "uninteresting" scene changes such as the repositioning of a static object or the turning on of a light, without further undermining their ability to segment foreground objects, such as people, where they occlude the background for too long. To alleviate tradeoffs such as these, and, more broadly, to allow TAPPMOG segmentation results to be tailored to the specific needs of an application, we introduce a general framework for guiding pixel-level TAPPMOG evolution with feedback from "high-level" modules. Each such module can use pixel-wise maps of positive and negative feedback to attempt to impress upon the TAPPMOG some definition of foreground that is best expressed through "higher-level" primitives such as image region properties or semantics of objects and events. By pooling the foreground error corrections of many high-level modules into a shared, pixel-level TAPPMOG model in this way, we improve the quality of the foreground segmentation and the performance of all modules that make use of it. We show an example of using this framework with a TAPPMOG method and high-level modules that all rely on dense depth data from a stereo camera.
Year
DOI
Venue
2002
10.1007/3-540-47977-5_36
ECCV (3)
Keywords
Field
DocType
pixel-level tappmog model,mixture-of-gaussian background models,pixel-level tappmog evolution,high-level feedback,foreground error correction,tappmog method,general framework,high-level module,complex appearance,tappmog segmentation result,segment foreground object,foreground segmentation,negative feedback,error correction,mixture of gaussians
Computer vision,Stereo camera,Computer science,Segmentation,Pooling,Negative feedback,Gaussian,Artificial intelligence,Pixel,Machine learning,Semantics
Conference
Volume
ISSN
ISBN
2352
0302-9743
3-540-43746-0
Citations 
PageRank 
References 
110
15.94
11
Authors
1
Search Limit
100110
Name
Order
Citations
PageRank
Michael Harville136935.55