Moving Object Detection by Detecting Contiguous Outliers in the Low-Rank Representation

By Xiaowei Zhou, Can Yang, Weichuan Yu. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011

658 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/f63a796b46d710c301c9fe8e974dea30133eb627

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In Computer Vision and Pattern Recognition

Citations in the corpus

Robust Subspace Learning: Robust PCA, Robust Subspace Tracking, and Robust Subspace Recovery (2017, methodology): “…[52], Motion-assisted Spatiotemporal Clustering of Low-rank (MSCL) [65], Detecting Contiguous Outliers in the LOw-rank Representation (DECOLOR) [66], Low-rank Structured-Sparse Decomposition (LSD) [67], Total Variation RPCA (TVRPCA) [68], Spatiotemporal RPCA (SRPCA) [69], Robust Motion…”
Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: A Review for a Comparative Evaluation with a Large-Scale Dataset (2015, methodology): “net 2014 dataset show that SRPCA outperforms GoDec [462], GRASTA [143], DECOLOR [464], and RMAMC [419].”
Video Processing From Electro-Optical Sensors for Object Detection and Tracking in a Maritime Environment: A Survey (2016, methodology): “background model of [209] with other background models such as those using local ternary patterns and visual saliency.”
Traditional and recent approaches in background modeling for foreground detection: An overview (2014, methodology): “(2013)[327] Direct Robust Matrix Factorization (DRMF(18)) (1) Xiong et al.”