传感器(英文名称:transducer/sensor)是一种检测装置,能感受到被测量的信息,并能将感受到的信息,按一定规律变换成为电信号或其他所需形式的信息输出,以满足信息的传输、处理、存储、显示、记录和控制等要求。

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题目: Moving Objects Detection with a Moving Camera: A Comprehensive Review

摘要:

在大约30年的时间里,许多研究团队致力于在各种挑战性环境中检测移动对象的大挑战。最初的应用涉及静态相机,但随着移动传感器的兴起,对移动相机的研究也逐渐出现。在这项调查中,我们建议识别和分类在文献中发现的不同的现有的方法。为此,我们建议根据场景表示的选择:一个平面或多个部分来对这些方法进行分类。在这两类方法中,根据8种不同的方法进行分组:全景背景减法、双摄像头、运动补偿、子空间分割、运动分割、平面+视差、多平面、分块图像分割。本文介绍了静态相机的方法以及静态相机和移动相机的挑战。本文还对公开数据集和评价指标进行了研究。

作者简介:

Marie-Neige Chapel,2017年9月在里昂第一大学获得计算机科学博士学位,博士论文题目是“运动物体检测与运动相机”。研究重点是在摄像机运动引起的视频流中,运动物体的真实运动与视运动的区别。并且提出了一种新的方法,使用几何线索来分类特征点为静态或移动。通过对物体的静态估计,通过对三维欧几里得距离随时间的比较,可以区分运动的物体和运动相机视频流中的静态物体。个人主页:https://mnchapel.github.io/

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Robotic systems for retail have gained a lot of attention due to the labor-intensive nature of such business environments. Many tasks have the potential to be automated via intelligent robotic systems that have manipulation capabilities. For example, empty shelves can be replenished, stray products can be picked up or new items can be delivered. However, many challenges make the realization of this vision a challenge. In particular, robots are still too expensive and do not work out of the box. In this paper, we discuss a work-in-progress approach for enabling power-on-and-go robots in retail environments through a combination of active, physical sensors and passive, artificial sensors. In particular, we use low-cost hardware sensors in conjunction with machine learning techniques in order to generate high-quality environmental information. More specifically, we present a setup in which a standard monocular camera and Bluetooth low-energy yield a reliable robot system that can immediately be used after placing a couple of sensors in the environment. The camera information is used to synthesize accurate 3D point clouds, whereas the BLE data is used to integrate the data into a complex map of the environment. The combination of active and passive sensing enables high-quality sensing capabilities at a fraction of the costs traditionally associated with such tasks.

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Robotic systems for retail have gained a lot of attention due to the labor-intensive nature of such business environments. Many tasks have the potential to be automated via intelligent robotic systems that have manipulation capabilities. For example, empty shelves can be replenished, stray products can be picked up or new items can be delivered. However, many challenges make the realization of this vision a challenge. In particular, robots are still too expensive and do not work out of the box. In this paper, we discuss a work-in-progress approach for enabling power-on-and-go robots in retail environments through a combination of active, physical sensors and passive, artificial sensors. In particular, we use low-cost hardware sensors in conjunction with machine learning techniques in order to generate high-quality environmental information. More specifically, we present a setup in which a standard monocular camera and Bluetooth low-energy yield a reliable robot system that can immediately be used after placing a couple of sensors in the environment. The camera information is used to synthesize accurate 3D point clouds, whereas the BLE data is used to integrate the data into a complex map of the environment. The combination of active and passive sensing enables high-quality sensing capabilities at a fraction of the costs traditionally associated with such tasks.

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