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首页> 外文会议>IEEE International Conference on Computer Vision Workshops >End-to-End Visual Target Tracking in Multi-robot Systems Based on Deep Convolutional Neural Network
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End-to-End Visual Target Tracking in Multi-robot Systems Based on Deep Convolutional Neural Network

机译：基于深度卷积神经网络的多机器人系统端到端视觉目标跟踪

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摘要

The problem of one-on-one target tracking from a single monocular image acquired from the viewpoint of a follower robot itself is studied in this paper. Previous works mainly depended on locating, onboard sensors with control mechanism, while robot may not carry advanced onboard equipment for localization or GNSS may also fail in GNSS-denied/Indoor environments. In this paper we propose a novel approach based on a deep convolutional neural network called Deep-Track, which trains a supervised image classifier only using images captured by the camera in the follower robot. Specifically, the Deep-Track system can output the estimated velocity of the target as well as the velocity control for the follower, by operating merely on two adjacent frames. In order to verify the effectiveness of Deep-Track, we build up a large-scale dataset in the simulator, in which the performance of the Deep-Track is evaluated and it is shown that a high tracking accuracy is achieved.
机译：本文研究了从跟随机器人自身角度获取的单眼单眼图像进行一对一目标跟踪的问题。先前的工作主要取决于具有控制机制的车载传感器的定位，而机器人可能未携带用于定位的先进车载设备，或者在GNSS受限/室内环境中GNSS可能也会失效。在本文中，我们提出了一种基于称为Deep-Track的深度卷积神经网络的新颖方法，该方法仅使用跟随者机器人中摄像机捕获的图像来训练监督图像分类器。具体地说，深度跟踪系统仅通过对两个相邻帧进行操作，就可以输出目标的估计速度以及跟随者的速度控制。为了验证Deep-Track的有效性，我们在模拟器中建立了一个大规模的数据集，在其中评估了Deep-Track的性能，并表明获得了很高的跟踪精度。

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