Human Activity Recognition Using Combinatorial Deep Belief Networks

Shreyank N. Gowda
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引用次数: 32

Abstract

Human activity recognition is a topic undergoing a great amount of research. The main reason for that is the number of practical applications that are developed using activity recognition as the base. This paper proposes an approach to human activity recognition using a combination of deep belief networks. One network is used to obtain features from motion and to do this we propose a modified Weber descriptor. Another network is used to obtain features from images and to do this we propose the modification of the standard local binary patterns descriptor to obtain a concatenated histogram of lower dimensions. This helps to encode spatial and temporal information of various actions happening in a frame. This further helps to overcome the dimensionality problem that occurs with LBP. The features extracted are then passed onto a CNN that classifies the activity. Few standard activities are considered such as walking, sprinting, hugging etc. Results showed that the proposed algorithm gave a high level of accuracy for classification.
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基于组合深度信念网络的人类活动识别
人类活动识别是一个正在进行大量研究的课题。其主要原因是以活动识别为基础开发的实际应用程序的数量。本文提出了一种结合深度信念网络的人类活动识别方法。一个网络用于从运动中获取特征,为此我们提出了一个改进的韦伯描述符。另一种网络用于从图像中获取特征,为此,我们提出了对标准局部二进制模式描述符的修改,以获得低维的连接直方图。这有助于对一个帧中发生的各种动作的空间和时间信息进行编码。这进一步有助于克服LBP中出现的维度问题。然后将提取的特征传递给对活动进行分类的CNN。很少有标准的活动被考虑,如散步、冲刺、拥抱等。结果表明,该算法具有较高的分类精度。
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