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首页> 外文期刊>Journal of Intelligent & Robotic Systems: Theory & Application >A Flow-based Motion Perception Technique for an Autonomous Robot System
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A Flow-based Motion Perception Technique for an Autonomous Robot System

机译：自主机器人系统中基于流的运动感知技术

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摘要

Visual motion perception from a moving observer is the most often encountered case in real life situations. It is a complex and challenging problem, although, it can promote the arising of new applications. This article presents an innovative and autonomous robotic system designed for active surveillance and a dense optical flow technique. Several optical flow techniques have been proposed for motion perception however, most of them are too computationally demanding for autonomous mobile systems. The proposed HybridTree method is able to identify the intrinsic nature of the motion by performing two consecutive operations: expectation and sensing. Descriptive properties of the image are retrieved using a tree-based scheme and during the expectation phase. In the sensing operation, the properties of image regions are used by a hybrid and hierarchical optical flow structure to estimate the flow field. The experiments prove that the proposed method extracts reliable visual motion information in a short period of time and is more suitable for applications that do not have specialized computer devices. Therefore, the HybridTree differs from other techniques since it introduces a new perspective for the motion perception computation: high level information about the image sequence is integrated into the estimation of the optical flow. In addition, it meets most of the robotic or surveillance demands and the resulting flow field is less computationally demanding comparatively to other state-of-the-art methods.
机译：在现实生活中，最经常遇到的情况是来自移动的观察者的视觉运动感知。尽管这可以促进新应用程序的兴起，但这是一个复杂而具有挑战性的问题。本文介绍了一种创新的，自主的机器人系统，该系统设计用于主动监视和密集光流技术。已经提出了几种用于运动感知的光流技术，但是，其中大多数对于自主移动系统在计算上都要求很高。所提出的HybridTree方法能够通过执行两个连续的操作来识别运动的内在本质：期望和感知。使用基于树的方案并在期望阶段检索图像的描述性属性。在感测操作中，混合层次光学流结构使用图像区域的属性来估计流场。实验证明，所提出的方法能够在短时间内提取出可靠的视觉运动信息，并且更适合于没有专用计算机设备的应用。因此，HybridTree与其他技术有所不同，因为它为运动感知计算引入了新的视角：有关图像序列的高级信息已集成到光流的估计中。此外，它可以满足大多数机器人或监视需求，并且与其他最新方法相比，所产生的流场对计算的需求较少。

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