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» Object Classification in Visual Surveillance Using Adaboost
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TIP
2008
344views more  TIP 2008»
13 years 8 months ago
A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications
Detection of moving objects in video streams is the first relevant step of information extraction in many computer vision applications. Aside from the intrinsic usefulness of being...
Lucia Maddalena, Alfredo Petrosino
IIHMSP
2006
IEEE
136views Multimedia» more  IIHMSP 2006»
14 years 2 months ago
Boosted String Representation and Its Application to Video Surveillance
This paper presents a new behavior classification system for analyzing human movements directly from video sequences. First of all, we propose a triangulation-based method to tran...
Yung-Tai Hsu, Jun-Wei Hsieh
TCSV
2008
174views more  TCSV 2008»
13 years 8 months ago
A Survey of Vision-Based Trajectory Learning and Analysis for Surveillance
Abstract--This paper presents a survey of trajectory-based activity analysis for visual surveillance. It describes techniques that use trajectory data to define a general set of ac...
Brendan Tran Morris, Mohan M. Trivedi
FLAIRS
2006
13 years 10 months ago
Using Validation Sets to Avoid Overfitting in AdaBoost
AdaBoost is a well known, effective technique for increasing the accuracy of learning algorithms. However, it has the potential to overfit the training set because its objective i...
Tom Bylander, Lisa Tate
ACIVS
2007
Springer
14 years 16 days ago
Patch-Based Experiments with Object Classification in Video Surveillance
We present a patch-based algorithm for the purpose of object classification in video surveillance. Within detected regions-of-interest (ROIs) of moving objects in the scene, a feat...
Rob G. J. Wijnhoven, Peter H. N. de With