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http://dx.doi.org/10.6109/jkiice.2020.24.12.1574

2D and 3D Hand Pose Estimation Based on Skip Connection Form  

Ku, Jong-Hoe (Department of Computer Engineering, Pusan National University)
Kim, Mi-Kyung (Software Education Center, Pusan National University)
Cha, Eui-Young (Department of Computer Engineering, Pusan National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.24, no.12, 2020 , pp. 1574-1580 More about this Journal
Abstract
Traditional pose estimation methods include using special devices or images through image processing. The disadvantage of using a device is that the environment in which the device can be used is limited and costly. The use of cameras and image processing has the advantage of reducing environmental constraints and costs, but the performance is lower. CNN(Convolutional Neural Networks) were studied for pose estimation just using only camera without these disadvantage. Various techniques were proposed to increase cognitive performance. In this paper, the effect of the skip connection on the network was experimented by using various skip connections on the joint recognition of the hand. Experiments have confirmed that the presence of additional skip connections other than the basic skip connections has a better effect on performance, but the network with downward skip connections is the best performance.
Keywords
Hand joint estimation; Deep learning; Encoder-Decoder; Skip connection; U-Net;
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