A Method of Action Recognition in Ego-Centric Videos by Using Object-Hand Relations

Akihiro Matsufuji, Wei-Fen Hsieh, Hao-Ming Hung, Eri Shimokawara, Toru Yamaguchi, Lieu-Hen Chen
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引用次数: 1

Abstract

We present a system for integrating the neural networks' inference by using context and relation for complicated action recognition. In recent years, first person point of view which called as ego-centric video analysis draw a high attention to better understanding human activity and for being used to law enforcement, life logging and home automation. However, action recognition of ego-centric video is fundamental problem, and it is based on some complicating feature inference. In order to overcome these problems, we propose the context based inference for complicated action recognition. In realistic scene, people manipulate objects as a natural part of performing an activity, and these object manipulations are important part of the visual evidence that should be considered as context. Thus, we take account of such context for action recognition. Our system is consist of rule base architecture of bi-directional associative memory to use context of object-hand relationship for inference. We evaluate our method on benchmark first person video dataset, and empirical results illustrate the efficiency of our model.
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基于物手关系的自我中心视频动作识别方法
提出了一种基于上下文和关系的神经网络推理集成系统,用于复杂动作识别。近年来,第一人称视角被称为以自我为中心的视频分析,为更好地理解人类活动而受到高度关注,并被用于执法、生活记录和家庭自动化。然而,以自我为中心的视频的动作识别是一个基础问题,它基于一些复杂的特征推理。为了克服这些问题,我们提出了基于上下文的复杂动作识别方法。在现实场景中,人们对物体的操作是进行活动的自然组成部分,这些物体的操作是视觉证据的重要组成部分,应被视为上下文。因此,我们考虑到这样的上下文来进行动作识别。该系统由双向联想记忆的规则库架构组成,利用对象-手关系上下文进行推理。我们在基准第一人称视频数据集上对该方法进行了评估,实证结果证明了该模型的有效性。
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