使用低分辨率可穿戴视觉的高水平活动识别

Sudeep Sundaram, W. Mayol-Cuevas
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引用次数: 60

摘要

本文提出了一个系统,旨在作为可穿戴助手的启用平台。该方法通过可穿戴相机观察操作,并借助3级动态贝叶斯网络和自适应时间模板,从大致稳定的低分辨率图像(160 × 120像素)中对活动进行分类。我们的动机是探索健壮但计算成本低廉的视觉方法,以执行尽可能多的活动推理,而无需诉诸更复杂的物体或手检测器。给出了方法的描述和所获得的结果,以及在可穿戴视觉传感领域进一步工作的动机。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
High level activity recognition using low resolution wearable vision
This paper presents a system aimed to serve as the enabling platform for a wearable assistant. The method observes manipulations from a wearable camera and classifies activities from roughly stabilized low resolution images (160 × 120 pixels) with the help of a 3-level Dynamic Bayesian Network and adapted temporal templates. Our motivation is to explore robust but computationally inexpensive visual methods to perform as much activity inference as possible without resorting to more complex object or hand detectors. The description of the method and results obtained are presented, as well as the motivation for further work in the area of wearable visual sensing.
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