Human Gait Recognition based on Earth Movers Distance and Zernike Moments

Li, Guangcheng; Gong, Yongyi; Yuan, Wei

doi:10.1016/j.procir.2016.10.090

Cited by 1 publication

(1 citation statement)

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“…Deng et al proposed the method of deep gait and used the pretraining model VGG-16 to obtain the feature representation of gait map [ 4 ]. Li et al proposed to learn the similarity between gait diagrams directly through the depth CNN (convolutional neural network) and extract the features of gait diagrams through CNN for matching recognition [ 5 ]. Zhao et al extracted the information of human motion shape by Canny edge detection to represent the information of motion edge and then achieved the purpose of human motion recognition by matching similar edges [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Recognition and Analysis of Gait Contour of Dance Movements Based on Generative Adversarial Networks

Ren

Park

2022

Computational Intelligence and Neuroscience

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With the generation of images, videos, and other data, how to identify the gait of the action in the video has gradually become the focus of research. Aiming at the problems of complex and changeable movements, strong coherence, and serious occlusion in dance video images, this paper proposes a dynamic recognition model of gait contour of dance movements based on GAN (generative adversarial networks). GAN method is used to convert the gait diagrams in any state into a group of gait diagrams in normal state with multiple angles, which are arranged in turn. In order to retain as much original feature information as possible, multiple loss strategy is adopted to optimize the network, increase the distance between classes, and reduce the distance within classes. Experimental results show that the average recognition rates of this model at 50°, 90°, and 120°are 93.24, 98.24, and 97.93, respectively, which shows that the recognition accuracy of dance movement recognition method is high. And this method can effectively improve the dynamic recognition of gait contour of dance movements.

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Section: Introductionmentioning

confidence: 99%