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

TL;DR

This paper introduces DECOLOR, a unified low-rank representation method for moving object detection that effectively handles complex scenarios like nonrigid motion and dynamic backgrounds without requiring prior training data.

Contribution

DECOLOR unifies object detection and background modeling into a single optimization framework, improving detection in complex scenarios compared to existing methods.

Findings

DECOLOR outperforms state-of-the-art methods on simulated and real data.

It effectively handles nonrigid motion and dynamic backgrounds.

The algorithm is efficient and suitable for complex video scenarios.

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.