NDLI: Object tracking using incremental 2D-LDA learning and Bayes inference

Content Provider	IEEE Xplore Digital Library
Author	Guorong Li Dawei Liang Qingming Huang Shuqiang Jiang Wen Gao
Copyright Year	2008
Description	Author affiliation: Key Lab. of Intell. Info. Process., Inst. of Comput. Tech., Beijing (Shuqiang Jiang) \|\| Inst. of Digital Media, Peking Univ., Beijing (Wen Gao) \|\| Grad. Univ. of Chinese Acad. of Sci.(CAS), Beijing (Guorong Li; Dawei Liang; Qingming Huang)
Abstract	The appearances of the tracked object and its surrounding background usually change during tracking. As for tracking methods using subspace analysis, fixed subspace basis tends to cause tracking failure. In this paper, a novel tracking method is proposed by using incremental 2D-LDA learning and Bayes inference. Incremental 2D-LDA formulates object tracking as online classification between foreground and background. It updates the row- or/and column- projected matrix efficiently. Based on the current object location and the prior knowledge, the possible locations of the object (candidates) in the next frame are predicted using simple sampling method. Applying 2D-LDA projection matrix and Bayes inference, candidate that maximizes the posterior probability is selected as the target object. Moreover, informative background samples are selected to update the subspace basis. Experiments are performed on image sequences with the object's appearance variations due to pose, lighting, etc. We also make comparison to incremental 2D-PCA and incremental FDA. The experimental results demonstrate that the proposed method is efficient and outperforms both the compared methods.
Starting Page	1568
Ending Page	1571
File Size	469988
Page Count	4
File Format	PDF
ISBN	9781424417650
ISSN	15224880
DOI	10.1109/ICIP.2008.4712068
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Target tracking Principal component analysis Linear discriminant analysis Scattering Matrix converters Sampling methods Flowcharts Computer science Content addressable storage Failure analysis Bayes inference object tracking incremental 2D-LDA
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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