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Unsupervised flow-based motion analysis for an autonomous moving system

机译：自主运动系统的无监督基于流的运动分析

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摘要

This article discusses the motion analysis based on dense optical flow fields and for a new generation of robotic moving systems with real-time constraints. It focuses on a surveillance scenario where an especially designed autonomous mobile robot uses a monocular camera for perceiving motion in the environment. The computational resources and the processing-time are two of the most critical aspects in robotics and therefore, two non-parametric techniques are proposed, namely, the Hybrid Hierarchical Optical Flow Segmentation and the Hybrid Density-Based Optical Flow Segmentation. Both methods are able to extract the moving objects by performing two consecutive operations: refining and collecting. During the refining phase, the flow field is decomposed in a set of clusters and based on descriptive motion properties. These properties are used in the collecting stage by a hierarchical or density-based scheme to merge the set of clusters that represent different motion models. In addition, a model selection method is introduced. This novel method analyzes the flow field and estimates the number of distinct moving objects using a Bayesian formulation. The research evaluates the performance achieved by the methods in a realistic surveillance situation. The experiments conducted proved that the proposed methods extract reliable motion information in real-time and without using specialized computers. Moreover, the resulting segmentation is less computationally demanding compared to other recent methods and therefore, they are suitable for most of the robotic or surveillance applications.
机译：本文讨论了基于密集光流场的运动分析，以及具有实时约束的新一代机器人运动系统。它着重于一种监视场景，其中特别设计的自主移动机器人使用单眼相机来感知环境中的运动。计算资源和处理时间是机器人技术中最关键的两个方面，因此，提出了两种非参数技术，即混合分层光流分割和基于混合密度的光流分割。两种方法都可以通过执行两个连续的操作来提取运动对象：精炼和收集。在精炼阶段，将流场分解为一组簇，并基于描述性运动属性进行分解。这些属性在收集阶段由分层或基于密度的方案使用，以合并代表不同运动模型的群集集。另外，介绍了一种模型选择方法。这种新颖的方法分析了流场，并使用贝叶斯公式估算了不同运动对象的数量。该研究评估了在实际监视情况下这些方法所实现的性能。进行的实验证明，所提出的方法无需使用专用计算机即可实时提取可靠的运动信息。此外，与其他最近的方法相比，所得到的分割对计算的要求较低，因此，它们适用于大多数机器人或监视应用程序。

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  • 来源
    《Image and Vision Computing》 |2014年第7期|391-404|共14页
  • 作者

    Andry Maykol Pinto; Miguel V. Correia; A. Paulo Moreira; Paulo G. Costa;

  • 作者单位

    INESC Technology and Science, Faculty of Engineering of University of Porto, Rua Dr. Roberto Frias, s 4200-465 Porto, Portugal,Departamento de Electrotecnia da Faculdade de Engenharia da Universidade do Porto, Rua Dr. Roberto Frias, s 4200-465 Porto, Portugal;

    INESC Technology and Science, Faculty of Engineering of University of Porto, Rua Dr. Roberto Frias, s 4200-465 Porto, Portugal;

    INESC Technology and Science, Faculty of Engineering of University of Porto, Rua Dr. Roberto Frias, s 4200-465 Porto, Portugal;

    INESC Technology and Science, Faculty of Engineering of University of Porto, Rua Dr. Roberto Frias, s 4200-465 Porto, Portugal;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Motion segmentation; Optical flow; Moving observer; Active surveillance; Mobile robot;

    机译：运动分割光流移动的观察者;主动监视;移动机器人;

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