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）：无法访问印刷标志的信息直接影响美国超过120万盲人的移动性独立性，许多先前提出的针对此问题的技术解决方案需要对环境进行物理修改（谈话的标志或编码标记的放置），或者要求用户携带专用计算设备，而这些设备可以携带特定的计算设备，而这些设备可以固定。最近追求的策略是利用计算机视觉中智能手机和技术的计算能力，以允许盲人使用市售，不耻辱的智能手机在远距离阅读标志。然而，尽管存在复杂的算法是为了从混乱的视频输入中识别和提取符号文本（例如，通过绘制诸如Google Maps（例如，在街道视图中）自动找到并仅模糊的车牌文本绘制诸如Google Maps之类的服务）当前的移动解决方案用于阅读符号 距离的文字相对较差。这种糟糕的性能很大程度上是因为直到最近，智能手机处理器根本无法以实时速率执行最先进的计算机视觉文本提取和识别算法，这迫使以前的移动符号读取器使用较旧，简单，效率较低的算法。下一代智能手机以根本不同的混合处理器体系结构（例如Tegra 4，Snapdragon 800，均于2013年发行），均以专用的嵌入式图形处理单元（GPU）和多核CPUS运行，这使它们非常适合高表演，视觉，稳定的计算。在这项研究中，我们建议开发一种基于智能手机的系统，用于查找和阅读距离的距离，通过在现代智能手机混合GPU/CPU处理器架构上实施最先进的文本提取算法，从而极大地超过了以前的读者。在第一阶段，将与盲人用户一起开发和测试拟议的系统。在第二阶段中，用户测试的反馈将集成到系统设计中，并且将改进性能，以允许在极具挑战性（例如低光）环境中运行。