摘要翻译：
在自动驾驶的背景下，在计算机发出接管请求的情况下，人类可能需要接管，实现驾驶安全的关键一步是监控手，以确保司机为这种请求做好准备。这项工作，侧重于这一过程的第一步，即定位手。这样的系统必须在各种恶劣的光照条件下实时工作。本文在原有的OpenPose工作的基础上，提出了一种快速的ConvNet方法，用于全身关节的估计。该网络以较少的参数进行修改，并使用我们自己的日间自然主义自动驾驶数据集进行重新训练，以估计驾驶员和乘客手腕和肘部的关节和亲和力热图，总共8个关节类别和每个腕肘对之间的部分亲和力场。该方法在多名司机和乘客身上以40 fps的速度实时运行真实世界的数据。该系统在定性和定量方面都得到了广泛的评价，在关节定位和臂角估计方面显示了至少95%的检测性能。
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英文标题：
《Looking at Hands in Autonomous Vehicles: A ConvNet Approach using Part
  Affinity Fields》
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作者：
Kevan Yuen and Mohan M. Trivedi
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最新提交年份：
2018
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Image and Video Processing        图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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一级分类：Computer Science        计算机科学
二级分类：Computer Vision and Pattern Recognition        计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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英文摘要：
  In the context of autonomous driving, where humans may need to take over in the event where the computer may issue a takeover request, a key step towards driving safety is the monitoring of the hands to ensure the driver is ready for such a request. This work, focuses on the first step of this process, which is to locate the hands. Such a system must work in real-time and under varying harsh lighting conditions. This paper introduces a fast ConvNet approach, based on the work of original work of OpenPose for full body joint estimation. The network is modified with fewer parameters and retrained using our own day-time naturalistic autonomous driving dataset to estimate joint and affinity heatmaps for driver & passenger's wrist and elbows, for a total of 8 joint classes and part affinity fields between each wrist-elbow pair. The approach runs real-time on real-world data at 40 fps on multiple drivers and passengers. The system is extensively evaluated both quantitatively and qualitatively, showing at least 95% detection performance on joint localization and arm-angle estimation.
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PDF链接：
https://arxiv.org/pdf/1804.01176