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Lending an Artificial Eye: Beyond Evaluation of CV-Based Assistive Systems for Visually Impaired People

机译:借着人工的眼睛:超越评估基于CV的辅助系统,为视力受损人员

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The autonomy of the visually impaired, expressed by their ability to accomplish everyday tasks on their own even when help by others is not available, is of paramount importance. Hence, the rapid growth of computer vision in the last decade has given rise to a large number of assistive applications aiming to help the visually impaired in perceiving the world in a similar way to the seeing ones. In this study we investigate the usability of a hybrid system, named e-Vision, that couples the natural and seamless adoption provided by an external camera embedded on a pair of glasses with the processing power and the penetration rate of modern smartphone devices. The ultimate benefit which e-Vision hopes to bring to the visually impaired is greater autonomy, and the increased wellbeing. To assess e-Vision's performance, a month-long pilot study took place and people with actual visual impairment used the system in their daily lives. This procedure enable the system's evaluation under real conditions. Although the e-Vision's evaluation provided indications for a promising system, the obtained results were below our expectations with respect to practical usage. Since the employed computer vision modules were based on state-of-the-art deep learning models capable to achieve top-level performance, we identified the shortcomings and the limitations that typical computer vision-based practices set to the creation of assistive technologies for the visually impaired. Therefore, we propose potential remedies capable of overcoming the identified obstacles in existing practices.
机译:目前损害的自主权,即使没有其他人的帮助,也表达了他们自己完成日常任务的能力,这是至关重要的。因此,过去十年计算机视觉的快速增长使大量辅助应用程序旨在帮助视障人士以与看见者类似的方式感知世界。在这项研究中,我们调查混合系统的可用性,命名为E-Vision,这是通过嵌入一副眼镜的外部相机提供的自然和无缝采用,其具有处理能力和现代智能手机设备的渗透率。 E-Vieion希望带到视力受损的最终利益是更大的自主权,并且增加的福祉。为了评估E-Vision的表现,发生了一个月长的试点研究,并且具有实际视觉损伤的人们在日常生活中使用了该系统。此过程使系统在真实条件下的评估。虽然E-Vision的评价为有前途的系统提供了迹象,但所获得的结果低于我们对实际使用量的预期。由于所采用的计算机视觉模块基于能够实现顶级性能的最先进的深度学习模型,因此我们确定了缺点和基于计算机视觉的实践的缺点,该实践设定为创建辅助技术视力障碍。因此,我们提出了能够克服现有实践中所确定的障碍的潜在补救措施。

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