Xiaorui Zhang^1,2,3,*, Qijian Xie¹, Wei Sun^3,4, Yongjun Ren^1,2,3, Mithun Mukherjee⁵

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 47-61, 2023, DOI:10.32604/cmc.2023.042561 - 31 October 2023

Abstract Fall behavior is closely related to high mortality in the elderly, so fall detection becomes an important and urgent research area. However, the existing fall detection methods are difficult to be applied in daily life due to a large amount of calculation and poor detection accuracy. To solve the above problems, this paper proposes a dense spatial-temporal graph convolutional network based on lightweight OpenPose. Lightweight OpenPose uses MobileNet as a feature extraction network, and the prediction layer uses bottleneck-asymmetric structure, thus reducing the amount of the network. The bottleneck-asymmetrical structure compresses the number of input… More >

Jiabao Shi¹, Zhao Qiu^1,*, Tao Chen¹, Jiale Lin¹, Hancheng Huang², Yunlong He³, Yu Yang³

Journal of New Media, Vol.4, No.2, pp. 97-106, 2022, DOI:10.32604/jnm.2022.027176 - 13 June 2022

Abstract Big data is a comprehensive result of the development of the Internet of Things and information systems. Computer vision requires a lot of data as the basis for research. Because skeleton data can adapt well to dynamic environment and complex background, it is used in action recognition tasks. In recent years, skeleton-based action recognition has received more and more attention in the field of computer vision. Therefore, the keypoints of human skeletons are essential for describing the pose estimation of human and predicting the action recognition of the human. This paper proposes a skeleton point More >