使用自动对象和凝视检测来描述真实世界交互中的共同注意行为

Pranav Venuprasad, Tushar Dobhal, Anurag Paul, Tu N. Nguyen, A. Gilman, P. Cosman, L. Chukoskie
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引用次数: 9

摘要

联合注意是儿童发展过程中必不可少的一部分,联合注意障碍被认为是自闭症的早期症状之一。在本文中,我们开发了一种新的技术,通过研究两个人类受试者彼此之间以及与房间中存在的多个物体之间的相互作用,来实时表征共同注意力。这是通过眼球追踪眼镜捕捉受试者的目光,并在预定义的指示对象上检测他们的表情来完成的。通过处理安装在眼球追踪眼镜上的世界观摄像头的视频馈送,训练并部署了一个深度学习网络,以检测受试者视野内的物体。确定受试者的注视模式,并在检测到共同注意时提供实时音频响应,即当他们的注视一致时。我们的研究结果表明,在准确性测量(外观阳性预测值)和各种系统参数的联合外观检测延迟之间存在权衡。对于更精确的关节外观检测,系统具有更高的延迟,而对于更快的检测,系统的检测精度会下降。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Characterizing joint attention behavior during real world interactions using automated object and gaze detection
Joint attention is an essential part of the development process of children, and impairments in joint attention are considered as one of the first symptoms of autism. In this paper, we develop a novel technique to characterize joint attention in real time, by studying the interaction of two human subjects with each other and with multiple objects present in the room. This is done by capturing the subjects' gaze through eye-tracking glasses and detecting their looks on predefined indicator objects. A deep learning network is trained and deployed to detect the objects in the field of vision of the subject by processing the video feed of the world view camera mounted on the eye-tracking glasses. The looking patterns of the subjects are determined and a real-time audio response is provided when a joint attention is detected, i.e., when their looks coincide. Our findings suggest a trade-off between the accuracy measure (Look Positive Predictive Value) and the latency of joint look detection for various system parameters. For more accurate joint look detection, the system has higher latency, and for faster detection, the detection accuracy goes down.
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