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231224s2013 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2012.132
|2 doi
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|a pubmed24n0728.xml
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|a eng
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| 100 |
1 |
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|a Zhou, Xiaowei
|e verfasserin
|4 aut
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| 245 |
1 |
0 |
|a Moving Object Detection by Detecting Contiguous Outliers in the Low-Rank Representation
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| 264 |
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|c 2013
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| 336 |
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|a Text
|b txt
|2 rdacontent
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| 337 |
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|a ƒaComputermedien
|b c
|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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| 500 |
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|a Date Completed 25.11.2015
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| 500 |
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|a Date Revised 10.09.2015
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| 500 |
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a Object detection is a fundamental step for automated video analysis in many vision applications. Object detection in a video is usually performed by object detectors or background subtraction techniques. Often, an object detector requires manually labeled examples to train a binary classifier, while background subtraction needs a training sequence that contains no objects to build a background model. To automate the analysis, object detection without a separate training phase becomes a critical task. People have tried to tackle this task by using motion information. But existing motion-based methods are usually limited when coping with complex scenarios such as nonrigid motion and dynamic background. In this paper, we show that the above challenges can be addressed in a unified framework named DEtecting Contiguous Outliers in the LOw-rank Representation (DECOLOR). This formulation integrates object detection and background learning into a single process of optimization, which can be solved by an alternating algorithm efficiently. We explain the relations between DECOLOR and other sparsity-based methods. Experiments on both simulated data and real sequences demonstrate that DECOLOR outperforms the state-of-the-art approaches and it can work effectively on a wide range of complex scenarios
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| 650 |
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4 |
|a Journal Article
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| 650 |
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4 |
|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a Yang, Can
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yu, Weichuan
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 35(2013), 3 vom: 21. März, Seite 597-610
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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| 773 |
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|g volume:35
|g year:2013
|g number:3
|g day:21
|g month:03
|g pages:597-610
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|u http://dx.doi.org/10.1109/TPAMI.2012.132
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