Understanding egocentric activities

A. Fathi, Ali Farhadi, James M. Rehg
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引用次数: 400

Abstract

We present a method to analyze daily activities, such as meal preparation, using video from an egocentric camera. Our method performs inference about activities, actions, hands, and objects. Daily activities are a challenging domain for activity recognition which are well-suited to an egocentric approach. In contrast to previous activity recognition methods, our approach does not require pre-trained detectors for objects and hands. Instead we demonstrate the ability to learn a hierarchical model of an activity by exploiting the consistent appearance of objects, hands, and actions that results from the egocentric context. We show that joint modeling of activities, actions, and objects leads to superior performance in comparison to the case where they are considered independently. We introduce a novel representation of actions based on object-hand interactions and experimentally demonstrate the superior performance of our representation in comparison to standard activity representations such as bag of words.
理解自我中心活动
我们提出了一种方法来分析日常活动,如膳食准备,使用视频从自我中心相机。我们的方法执行关于活动、动作、手和对象的推理。日常活动是活动识别的一个具有挑战性的领域,非常适合以自我为中心的方法。与以前的活动识别方法相比,我们的方法不需要预先训练物体和手的检测器。相反,我们展示了通过利用来自自我中心环境的物体、手和动作的一致外观来学习活动分层模型的能力。我们表明,与独立考虑活动、动作和对象的情况相比,活动、动作和对象的联合建模可以带来更好的性能。我们引入了一种新的基于对象-手交互的动作表示,并通过实验证明了与标准活动表示(如单词袋)相比,我们的表示具有优越的性能。
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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