| Visual Tracking with Online Multiple Instance Learning. | |
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MedLine Citation:
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PMID: 21173445 Owner: NLM Status: Publisher |
Abstract/OtherAbstract:
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In this paper we address the problem of tracking an object in a video given its location in the first frame and no other information. Recently, a class of tracking techniques called "tracking by detection" has been shown to give promising results at real-time speeds. These methods train a discriminative classifier in an online manner to separate the object from the background. This classifier bootstraps itself by using the current tracker state to extract positive and negative examples from the current frame. Slight inaccuracies in the tracker can therefore lead to incorrectly labeled training examples, which degrade the classifier and can cause further drift. In this paper we show that using Multiple Instance Learning (MIL) instead of traditional supervised learning avoids these problems, and can therefore lead to a more robust tracker with fewer parameter tweaks. We propose a novel online MIL algorithm for object tracking that achieves superior results with real-time performance. We present thorough experimental results (both qualitative and quantitative) on a number of challenging video clips. |
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Authors:
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Boris Babenko; Ming-Hsuan Yang; Serge Belongie |
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Publication Detail:
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Type: JOURNAL ARTICLE Date: 2010-12-14 |
Journal Detail:
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Title: IEEE transactions on pattern analysis and machine intelligence Volume: - ISSN: 1939-3539 ISO Abbreviation: - Publication Date: 2010 Dec |
Date Detail:
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Created Date: 2010-12-21 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 9885960 Medline TA: IEEE Trans Pattern Anal Mach Intell Country: - |
Other Details:
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Languages: ENG Pagination: - Citation Subset: - |
Affiliation:
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University of California, San Diego, San Diego. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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