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» Classifier Grids for Robust Adaptive Object Detection
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IROS
2008
IEEE
144views Robotics» more  IROS 2008»
14 years 1 months ago
Frame rate object extraction from video sequences with self organizing networks and statistical background detection
— In many computer vision related applications it is necessary to distinguish between the background of an image and the objects that are contained in it. This is a difficult pr...
Thiago C. Bellardi, Dizan Vasquez, Christian Laugi...
AVSS
2009
IEEE
13 years 8 months ago
Robust Vehicle Detection for Tracking in Highway Surveillance Videos Using Unsupervised Learning
This paper presents a novel approach to vehicle detection in highway surveillance videos. This method incorporates well-studied computer vision and machine learning techniques to ...
Birgi Tamersoy, Jake K. Aggarwal
ICPR
2010
IEEE
13 years 5 months ago
Robust Foreground Object Segmentation via Adaptive Region-Based Background Modelling
We propose a region-based foreground object segmentation method capable of dealing with image sequences containing noise, illumination variations and dynamic backgrounds (as often...
Vikas Reddy, Conrad Sanderson, Brian C. Lovell
IJCNN
2006
IEEE
14 years 1 months ago
A computational intelligence-based criterion to detect non-stationarity trends
—The stationarity hypothesis is largely and implicitly assumed when designing classifiers (especially those for industrial applications) but it does not generally hold in practic...
Cesare Alippi, Manuel Roveri
CVPR
1998
IEEE
14 years 9 months ago
Using Adaptive Tracking to Classify and Monitor Activities in a Site
We describe a vision system that monitors activity in a site over extended periods of time. The system uses a distributed set of sensors to cover the site, and an adaptive tracker...
W. Eric L. Grimson, Chris Stauffer, R. Romano, L. ...