Abstract
Wearable cameras capture a first-person view of the daily activities of the camera wearer, offering a visual diary of the user behaviour. Detection of the appearance of people the camera user interacts with for social interactions analysis is of high interest. Generally speaking, social events, lifestyle and health are highly correlated, but there is a lack of tools to monitor and analyse them. We consider that egocentric vision provides a tool to obtain information and understand users social interactions. We propose a model that enables us to evaluate and visualize social traits obtained by analysing social interactions appearance within egocentric photostreams. Given sets of egocentric images, we detect the appearance of faces within the days of the camera wearer, and rely on clustering algorithms to group their feature descriptors in order to re-identify persons. Recurrence of detected faces within photostreams allows us to shape an idea of the social pattern of behaviour of the user. We validated our model over several weeks recorded by different camera wearers. Our findings indicate that social profiles are potentially useful for social behaviour interpretation.
Abstract (translated)
可穿戴式相机可捕捉第一人称视角,了解佩戴相机者的日常活动,提供用户行为的可视日记。对相机用户与之互动的人的外貌进行社会互动分析是一项很有意义的工作。一般来说,社会事件、生活方式和健康之间有着高度的相关性,但缺乏对其进行监测和分析的工具。我们认为,以自我为中心的愿景提供了一种获取信息和理解用户社交互动的工具。我们提出了一个模型,使我们能够评估和形象化的社会特征,通过分析社会互动现象在自我中心的光流。给定一组以自我为中心的图像,我们可以在佩戴相机的几天内检测出人脸的外观,并依靠聚类算法对其特征描述符进行分组,以便重新识别人。检测到的人脸在照片流中的重复出现使我们能够形成用户行为的社会模式。我们在几个星期内验证了由不同的相机佩戴者记录的模型。我们的研究结果表明,社会概况对解释社会行为有潜在的帮助。
URL
https://arxiv.org/abs/1905.04073