摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-调查人员发布了关于机器人的新报告。根据NewsRx记者从克罗地亚萨格勒布发回的新闻报道，研究人员称：“本文提出了一个用于水下人-机器人交互的潜水姿态图像数据集，通过提供该数据集，本文旨在研究利用自主水下航行器(AUV)的潜水姿态视觉检测作为与人类潜水员通信的基础的潜力。”这项研究的财政支持来自海军研究办公室。我们的新闻编辑从扎格雷b大学的研究中获得了一句话，“除了图像记录，”采用SM ART手势识别手套记录同一数据集，手套采用介电弹性体传感器和O N板处理来确定所选择的手势，并通过声学将与手势相关的命令传递给AUV，虽然该方法可以在不同的能见度条件下，甚至在没有视线的情况下使用，它引入了GE Sture命令的声音传输所需的通信延迟。为了比较效率,手套配备了视觉标记,该标记采用了一种基于手势的语言CADDIAN,并与手套的机载识别过程并行地用水下摄像机记录。数据集包含超过30000个该数据是由5名不同的潜水员和5名处于泳池状态的潜水员组成，分别在离相机1米、2米和3米的地方记录下来的。手套手势识别统计数据是根据潜水员的平均反应时间来报告的。"执行手势所需的时间、识别成功率、传输时间等等."
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Ro botics. According to news reporting originating from Zagreb, Croatia, by NewsRx correspondents, research stated, "In this paper, we present a dataset of diving gesture images used for human-robot interaction underwater. By offering this ope n access dataset, the paper aims at investigating the potential of using visual detection of diving gestures from an autonomous underwater vehicle (AUV) as a fo rm of communication with a human diver." Financial support for this research came from Office of Naval Research. Our news editors obtained a quote from the research from the University of Zagre b, "In addition to the image recording, the same dataset was recorded using a sm art gesture recognition glove. The glove uses dielectric elastomer sensors and o n -board processing to determine the selected gesture and transmit the command a ssociated with the gesture to the AUV via acoustics. Although this method can be used under different visibility conditions and even without line of sight, it i ntroduces a communication delay required for the acoustic transmission of the ge sture command. To compare efficiency, the glove was equipped with visual markers proposed in a gesture -based language called CADDIAN and recorded with an under water camera in parallel to the glove's onboard recognition process. The dataset contains over 30,000 underwater frames of nearly 900 individual gestures annota ted in corresponding snippet folders. The dataset was recorded in a balanced rat io with five different divers in sea and five different divers in pool condition s, with gestures recorded at 1, 2 and 3 metres from the camera. The glove gestur e recognition statistics are reported in terms of average diver reaction time, a verage time taken to perform a gesture, recognition success rate, transmission t imes and more."
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