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Human detection and following by a mobile robot using 3D features

机译:使用3D功能的移动机器人进行人类检测及以下

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Human-robot interaction is one of the most basic requirements for service robots. In order to provide the desired service, these robots are required to detect and track human beings in the environment. This paper presents a novel approach for classifying a target person in a crowded environment. The system used the approaches for human detection and following by implementing the multi-sensor data fusion technique using stereo camera and laser range finder (LRF). Our system tracks human being by gathering features of human upper body and face in 3D space from stereo camera and uses laser rangefinder to get legs data. Using these data our system classifies the target person from other human beings in the environment. We used Haar cascade classifiers for the detection of upper body and face, and used stereo camera for getting dimensions in 3D space. The approach for gathering legs data is based on the recognition of legs pattern extracted from laser scan. Tracking of target person is done using Cam Shift theorem. Using all these techniques we present a novel approach for target person classification and tracking. Our approach is feasible for mobile robots with an identical device arrangement.
机译:人机互动是服务机器人最基本的要求之一。为了提供所需的服务,需要这些机器人来检测和跟踪环境中的人。本文提出了一种在拥挤环境中对目标人进行分类的新方法。该系统使用使用立体声相机和激光测距仪(LRF)实现多传感器数据融合技术的人体检测方法。我们的系统通过从立体声相机收集人体上身和面部的3D空间的特征来追踪人类,并使用激光测距仪获得腿部数据。使用这些数据我们的系统将目标人员分类为环境中的其他人。我们使用HAAR级联分类器来检测上身和面部,并使用立体声相机用于在3D空间中获得尺寸。收集腿部数据的方法基于从激光扫描中提取的腿部图案的识别。使用凸轮移位定理完成目标人员的跟踪。使用所有这些技术,我们提出了一种用于目标人分类和跟踪的新方法。我们的方法对于具有相同设备排列的移动机器人是可行的。

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