Sign Finding and Reading SFAR on GPU Accelerated Mobile Devices

在 GPU 加速的移动设备上查找和读取 SFAR 的标志

• 批准号: 8779810
• 负责人: Christian Bruccoleri
• 金额: $ 22.97万
• 依托单位: LYNNTECH, INC.
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-30 至 2017-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8779810
• 关键词: Acceleration; Access to Information; Algorithms; Antirrhinum; Architecture; Back; Code; Computer Vision Systems; Distant; Environment; Equipment; Eye; Feedback; Hybrids; Licensing; Light; Literature; Maps; Modification; Performance; Phase; Population; Printing; Process; Quality of life; Reader; Reading; Research; Research Institute; Risk; Running; SKI gene; Self-Help Devices; Services; Solutions; System; Techniques; Telephone; Test Result; Testing; Text; Time; Vision; Visually Impaired Persons; assistive device/technology; authority; base; blind; design; experience; handheld mobile device; improved; next generation; operation; phase 1 study; public health relevance; volunteer

DESCRIPTION (provided by applicant): The inability to access information on printed signs directly impacts the mobility independence of the over 1.2 million blind persons in the U.S. Many previously proposed technological solutions to this problem either required physical modifications to the environment (talking signs or the placement of coded markers) or required the user to carry around specialized computational equipment, which can be stigmatizing. A recently pursued strategy is to utilize the computational capabilities of smart phones and techniques from computer vision to allow blind persons to read signs at a distance using commercially available, non-stigmatizing, smart- phones. However, despite the fact that sophisticated algorithms exist to recognize and extract sign text from cluttered video input (as evidenced, for example, by mapping services such as Google Maps automatically locating and blurring out only license plate text in street-view maps) current mobile solutions for reading sign text at a distance perform relatively poorly. This poor performance is largely because until recently, smart-phone processors have simply not been able to execute state-of-the-art computer vision text extraction and recognition algorithms at real-time rates, which forced previous mobile sign readers to utilize older, simplistic, less effective algorithms. Next-generation smart-phones run on fundamentally different, hybrid processor architectures (such as the Tegra 4, Snapdragon 800, both released in 2013) with dedicated embedded graphical processing units (GPUs) and multi-core CPUs, which make them ideal for high-performance, vision-heavy computation. In this study, we propose to develop a smart-phone-based system for finding and reading signs at a distance which significantly outperforms previous such readers by implementing state-of-the-art text extraction algorithms on modern smart-phone hybrid GPU/CPU processor architectures. In Phase I, the proposed system will be developed and tested with blind users. In Phase II, feedback from user testing will be integrated into system design and the performance will be improved to permit operation in extremely challenging (such as low light) environments.

描述(由申请人提供)：无法获取印刷标志上的信息直接影响到美国120多万盲人的行动独立性。许多以前提出的解决这一问题的技术解决方案要么需要对环境进行物理修改(会说话的标志或放置编码标志)，要么需要用户随身携带专门的计算设备，这可能会使人蒙羞。最近推行的一项战略是利用智能手机的计算能力和计算机视觉技术，允许盲人使用商业上可获得的、非污名化的智能手机阅读远处的标志。然而，尽管存在从杂乱的视频输入中识别和提取标志文本的复杂算法(例如，通过诸如谷歌地图等地图服务自动定位和模糊街景地图中的车牌文本来证明)，当前用于阅读标志的移动解决方案 距离较远的文本表现相对较差。这种糟糕的性能在很大程度上是因为直到最近，智能手机处理器还无法以实时速度执行最先进的计算机视觉文本提取和识别算法，这迫使以前的移动标志阅读器使用旧的、简单的、效率较低的算法。下一代智能手机运行在完全不同的混合处理器架构(如2013年发布的Tegra 4、Snapdrag800)上，配备专用嵌入式图形处理器(GPU)和多核CPU，这使它们成为高性能、视觉密集型计算的理想选择。在这项研究中，我们建议开发一个基于智能手机的系统，通过在现代智能手机混合GPU/CPU处理器架构上实现最先进的文本提取算法，实现远距离寻找和阅读标志的系统，其性能显著优于以往的此类阅读器。在第一阶段，拟议的系统将在盲人用户中开发和测试。在第二阶段，用户测试的反馈将被整合到系统设计中，性能将得到改善，以允许在极具挑战性的环境(如弱光)下运行。