Revisiting Blind Photography in the Context of Teachable Object Recognizers.

Kyungjun Lee, Jonggi Hong, Simone Pimento, Ebrima Jarjue, Hernisa Kacorri
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Abstract

For people with visual impairments, photography is essential in identifying objects through remote sighted help and image recognition apps. This is especially the case for teachable object recognizers, where recognition models are trained on user's photos. Here, we propose real-time feedback for communicating the location of an object of interest in the camera frame. Our audio-haptic feedback is powered by a deep learning model that estimates the object center location based on its proximity to the user's hand. To evaluate our approach, we conducted a user study in the lab, where participants with visual impairments (N = 9) used our feedback to train and test their object recognizer in vanilla and cluttered environments. We found that very few photos did not include the object (2% in the vanilla and 8% in the cluttered) and the recognition performance was promising even for participants with no prior camera experience. Participants tended to trust the feedback even though they know it can be wrong. Our cluster analysis indicates that better feedback is associated with photos that include the entire object. Our results provide insights into factors that can degrade feedback and recognition performance in teachable interfaces.

在可教物体识别器的背景下重新审视盲人摄影。
对于有视觉障碍的人来说,通过远程视力帮助和图像识别应用程序识别物体时,照片是必不可少的。可教对象识别器尤其如此,其识别模型是根据用户的照片进行训练的。在这里,我们提出了实时反馈功能,用于传达感兴趣的物体在相机画面中的位置。我们的音频-触觉反馈由深度学习模型驱动,该模型根据物体与用户手部的距离来估计物体中心位置。为了评估我们的方法,我们在实验室进行了一项用户研究,让有视觉障碍的参与者(N = 9)使用我们的反馈,在虚幻和杂乱的环境中训练和测试他们的物体识别器。我们发现,只有极少数照片未包含物体(2% 在虚幻环境中,8% 在杂乱环境中),即使是没有照相机使用经验的参与者,识别性能也很不错。参与者倾向于相信反馈,即使他们知道反馈可能是错误的。我们的聚类分析表明,反馈较好的照片与包含整个物体有关。我们的研究结果让我们深入了解了可能降低可教界面的反馈和识别性能的因素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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