NDLI: Human tracking based on Soft Decision Feature and online real boosting

Content Provider	IEEE Xplore Digital Library
Author	Yamashita, T. Fujiyoshi, H. Shihong Lao Kawade, M.
Copyright Year	2008
Description	Author affiliation: Chubu Univ., Kasugai, Japan (Fujiyoshi, H.) \|\| OMRON Corp., Kyoto, Japan (Yamashita, T.; Shihong Lao; Kawade, M.)
Abstract	Online boosting is an effective incremental learning method which can update weak classifiers efficiently according to the object being tracked. It is a promising technique for online object tracking to adapt to the appearance variations of objects during tracking process. However, proposed online-boosting based tracking methods update and select weak classifiers from fixed the offline learned weak classifiers, which might not be an optimal selection for object appearance variations. In this paper, we propose a new feature adjusting strategy for online boosting called soft decision feature. We combine it with online real AdaBoost to achieve better tracking performance in scenes with human pose and posture variations. Experiment result demonstrates that it can successfully deal with the human posture variation scenes that conventional online boosting tracking methods fails to deal with.
Starting Page	1
Ending Page	4
File Size	312138
Page Count	4
File Format	PDF
ISBN	9781424421749
ISSN	10514651
DOI	10.1109/ICPR.2008.4761196
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-08
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Humans Boosting Shape Probability density function Learning systems Layout Target tracking Histograms
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition

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