The DOI matches its Crossref record; 68 of the 68 references it cites were found in a registry or an index.
Moving Object Detection by Detecting Contiguous Outliers in the Low-Rank Representation
Abstract
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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Traditional and recent approaches in background modeling for foreground detection: An overview
Bouwmans, T. (2014) · Computer Science Review · doi:10.1016/j.cosrev.2014.04.001 · Cited by 676
Bouwmans, T., Javed, S., Sultana, M., & Jung, S. K. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.04.024 · Cited by 396
Thung, K.-H., Yap, P.-T., Adeli, E., Lee, S.-W., & Shen, D. (2018) · Medical Image Analysis · doi:10.1016/j.media.2018.01.002 · Cited by 89
A Robust Moving Object Detection in Multi-Scenario Big Data for Video Surveillance
Chen, B.-H., Shi, L.-F., & Ke, X. (2019) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2018.2828606 · Cited by 58
Automatic Visual Defect Detection Using Texture Prior and Low-Rank Representation
Huangpeng, Q., Zhang, H., Zeng, X., & Huang, W. (2018) · IEEE Access · doi:10.1109/access.2018.2852663 · Cited by 53
Metadata
- Authors
- Zhou, X., Yang, C., & Yu, W.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2013-03
- Volume
- 35
- Issue
- 3
- Pages
- 597-610
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- OpenAlex
- W2059639989
- Cited by
- 648
- References
- 68
- FWCI
- 32.79
- Type
- journal article
- Language
- en
- Open access
- closed
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