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首页> 外文会议>IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops >HandyNet: A One-stop Solution to Detect, Segment, Localize Analyze Driver Hands
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HandyNet: A One-stop Solution to Detect, Segment, Localize Analyze Driver Hands

机译：HandyNet：检测，分段，本地化和分析司机手的一站式解决方案

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Tasks related to human hands have long been part of the computer vision community. Hands being the primary actuators for humans, convey a lot about activities and intents, in addition to being an alternative form of communication/interaction with other humans and machines. In this study, we focus on training a single feedforward convolutional neural network (CNN) capable of executing many hand related tasks that may be of use in autonomous and semi-autonomous vehicles of the future. The resulting network, which we refer to as HandyNet, is capable of detecting, segmenting and localizing (in 3D) driver hands inside a vehicle cabin. The network is additionally trained to identify handheld objects that the driver may be interacting with. To meet the data requirements to train such a network, we propose a method for cheap annotation based on chroma-keying, thereby bypassing weeks of human effort required to label such data. This process can generate thousands of labeled training samples in an efficient manner, and may be replicated in new environments with relative ease.
机译：与人类手相关的任务长期以来一直是计算机视觉社区的一部分。手是人类的主要执行器，除了作为与其他人类和机器互动的替代形式的互动形式/互动之外，还传达了很多关于活动和意图。在这项研究中，我们专注于培训能够执行许多可能在未来的自主和半自动车辆中使用的许多手相关任务的单一前馈卷积神经网络（CNN）。我们将其称为手动的所得到的网络能够在车厢内检测，分割和定位（在3D）驾驶员手中。另外培训网络以识别驱动程序可以与之交互的手持对象。为了满足培训此类网络的数据要求，我们提出了一种基于色度键控的廉价注释的方法，从而绕过了标记此类数据所需的人力努力。该过程可以以有效的方式生成数千个标记的训练样本，并且可以在新环境中进行相对容易地复制。

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