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

Source: [Moving Object Detection by Detecting Contiguous Outliers in the Low-Rank Representation](<https://www.sota2.com/research/paper/68fa608c1e209131ce9f001c>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T19:42:41.418Z

Authors: Xiaowei Zhou, Can Yang, Weichuan Yu

Published: 2011-09-05T13:08:24.000Z

## 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 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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1109.0882>)

[PDF](<https://arxiv.org/pdf/1109.0882.pdf>)