| Moving Object Detection by Detecting Contiguous Outliers in the Low-Rank Representation. | |
| | |
MedLine Citation:
|
PMID: 22689075 Owner: NLM Status: Publisher |
Abstract/OtherAbstract:
|
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 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. |
| | |
Authors:
|
Xiaowei Zhou; Can Yang; Weichuan Yu |
Related Documents
:
|
22881395 - General allee effect in two-species population biology. 22778605 - Robust foreground detection: a fusion of masked grey world, probabilistic gradient info... 22404465 - A new development: evolving concepts in leaf ontogeny. 22738845 - Simultaneous estimation of computer model parameters and model bias. 22516635 - Polyhedra recognition by hypothesis accumulation. 15876215 - Using integrated geospatial mapping and conceptual site models to guide risk-based envi... |
Publication Detail:
|
Type: JOURNAL ARTICLE Date: 2012-6-8 |
Journal Detail:
|
Title: IEEE transactions on pattern analysis and machine intelligence Volume: - ISSN: 1939-3539 ISO Abbreviation: - Publication Date: 2012 Jun |
Date Detail:
|
Created Date: 2012-6-12 Completed Date: - Revised Date: - |
Medline Journal Info:
|
Nlm Unique ID: 9885960 Medline TA: IEEE Trans Pattern Anal Mach Intell Country: - |
Other Details:
|
Languages: ENG Pagination: - Citation Subset: - |
Affiliation:
|
The Hong Kong University of Science and Technology, Hong Kong. |
Export Citation:
|
APA/MLA Format Download EndNote Download BibTex |
| MeSH Terms | |
Descriptor/Qualifier:
|
|
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
Previous Document: Multivalent porous silicon nanoparticles enhance the immune activation potency of agonistic CD40 ant...
Next Document: A Prototype Learning Framework using EMD: Application to Complex Scenes Analysis.