An Introduction to the 3rd Workshop on Egocentric (First-Person) Vision

An Introduction to the 3rd Workshop on Egocentric (First-Person) Vision
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第三届以自我为中心(第一人称)视觉研讨会简介

DOI:
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发表时间:
2014
期刊:
2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops
影响因子:
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通讯作者:
A. Fathi
A. Fathi
中科院分区:
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文献类型:
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作者:
Steve Mann;Kris M. Kitani;Yong Jae Lee;M. Ryoo;A. Fathi

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自我中心的视觉提供了一个独特的视角的视觉世界,本质上是以人为中心的。由于以自我为中心的相机安装在用户身上(通常在用户的头部),它们自然会从我们的日常互动中收集视觉信息,甚至可以实时地对这些信息采取行动(例如,用于视觉辅助)。我们认为,这种以人为中心的特征可以对我们处理中央计算机视觉任务的方式产生很大影响,例如视觉检测,识别,预测和社会行为分析。通过利用第一人称视角范式,最近在以下领域取得了进展,例如个性化视频摘要,理解社会显着性的概念,使用由内而外的摄像机(捕获眼睛凝视的摄像机和向外看的摄像机)进行活动分析,识别人类交互和建模注意力焦点。然而,在许多方面,人们才刚刚开始理解第一人称范式的全部潜力(和局限性)。在第三届关于自我中心(第一人称)视觉的研讨会上,我们召集研究人员讨论新兴主题,例如:视觉分析的个性化;社会行为建模;理解群体动态和互动;以自我为中心的视频作为大数据;第一人称机器人视觉;和自我图形用户界面(EUI)。
Egocentric vision provides a unique perspective of the visual world that is inherently human-centric. Since egocentric cameras are mounted on the user (typically on the user's head), they are naturally primed to gather visual information from our everyday interactions, and can even act on that information in real-time (e.g. for a vision aid). We believe that this human-centric characteristic of egocentric vision can have a large impact on the way we approach central computer vision tasks such as visual detection, recognition, prediction, and socio-behavioral analysis. By taking advantage of the first-person point-of-view paradigm, there have been recent advances in areas such as personalized video summarization, understanding concepts of social saliency, activity analysis with inside-out cameras (a camera to capture eye gaze and an outward-looking camera), recognizing human interactions and modeling focus of attention. However, in many ways people are only beginning to understand the full potential (and limitations) of the first-person paradigm. In the 3rd workshop on Egocentric (First-Person) Vision, we bring together researchers to discuss emerging topics such as: Personalization of visual analysis; Socio-behavioral modeling; Understanding group dynamics and interactions; Egocentric video as big data; First-person vision for robotics; and Egographical User Interfaces (EUIs).
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