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JBE, vol. 23, no. 5, pp.598-605, September, 2018

DOI: https://doi.org/10.5909/JBE.2018.23.5.598

Using Skeleton Vector Information and RNN Learning Behavior Recognition Algorithm

Mi-Kyung Kim and Eui-Young Cha

C.A E-mail: ddosun@ousan.ac.kr

Abstract:

Behavior awareness is a technology that recognizes human behavior through data and can be used in applications such as risk behavior through video surveillance systems. Conventional behavior recognition algorithms have been performed using the 2D camera image device or multi-mode sensor or multi-view or 3D equipment. When two-dimensional data was used, the recognition rate was low in the behavior recognition of the three-dimensional space, and other methods were difficult due to the complicated equipment configuration and the expensive additional equipment. In this paper, we propose a method of recognizing human behavior using only CCTV images without additional equipment using only RGB and depth information. First, the skeleton extraction algorithm is applied to extract points of joints and body parts. We apply the equations to transform the vector including the displacement vector and the relational vector, and study the continuous vector data through the RNN model. As a result of applying the learned model to various data sets and confirming the accuracy of the behavior recognition, the performance similar to that of the existing algorithm using the 3D information can be verified only by the 2D information.



Keyword: skeleton, feature vector, RNN, SELU, deep learning

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