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Study on Virtual Human skeleton System

Journal of Beijing Institute of Technology 2004年第4期13卷 365-368页

作者：郭巧李亦 Network Information Center Beijing Institute of Technology Beijing100081 China

A solution of virtual Human skeleton system is proposed. Some issues on integration of anatomical geometry, biodynamics and computer animation are studied. The detailed skeleton system model that incorporates the biodynamic and geometric characteristics of a Human skeleton system allows some performance studies in greater detail than that performed before. It may provide an effective and convenient way to analyze and evaluate the movement performance of a human body when the personalized anatomical data are used in the models. An example shows that the proposed solution is effective for the stated problems.

关键词： Human skeleton biodynamics computer animation

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Building Indoor Dangerous Behavior Recognition Based on LSTM-GCN with Attention Mechanism

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Computer Modeling in Engineering & Sciences 2023年第11期137卷 1773-1788页

作者： Qingyue Zhao Qiaoyu Gu Zhijun Gao Shipian Shao Xinyuan Zhang School of Management Shenyang Jianzhu UniversityShenyang110168China School of Information and Communication Engineering North University of ChinaTaiyuan030051China School of Information and Control Engineering Shenyang Jianzhu UniversityShenyang110168China

Building indoor dangerous behavior recognition is a specific application in the field of abnormal human recognition.A human dangerous behavior recognition method based on LSTM-GCN with attention mechanism(GLA)model was proposed aiming at the problem that the existing Human skeleton-based action recognition methods cannot fully extract the temporal and spatial features.The network connects GCN and LSTMnetwork in series,and inputs the skeleton sequence extracted by GCN that contains spatial information into the LSTM layer for time sequence feature extraction,which fully excavates the temporal and spatial features of the skeleton sequence.Finally,an attention layer is designed to enhance the features of key bone points,and Softmax is used to classify and identify dangerous behaviors.The dangerous behavior datasets are derived from NTU-RGB+D and Kinetics data sets.Experimental results show that the proposed method can effectively identify some dangerous behaviors in the building,and its accuracy is higher than those of other similar methods.

关键词： Human skeleton building indoor dangerous behaviors recognition graph convolution network long short term memory network attention mechanism

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Weakly supervised 2D human pose transfer

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Science China(Information Sciences) 2021年第11期64卷 36-51页

作者： Qian ZHENG Yajie LIU Zhizhao LIN Dani LISCHINSKI Daniel COHEN-OR Hui HUANG College of Computer Science and Software Engineering Shenzhen University School of Computer Science and Engineering The Hebrew University of Jerusalem

We present a novel method for pose transfer between two 2 D Human skeletons. When the bone lengths and proportions between the two skeletons are significantly different, pose transfer becomes a challenging task, which cannot be accomplished by simply copying the joint positions or the bone directions.Our data-driven approach utilizes a deep neural network trained, in a weakly supervised fashion, to encode a skeleton into two separate latent codes, one representing its pose, and another representing the skeleton’s proportions(skeleton-ID). The network is given two skeletons, and learns to combine the pose of one with the skeleton-ID of the other. Lacking supervision on the poses, we develop a novel loss that qualitatively compares poses of different skeletons. We evaluate the performance of our method on a large set of poses.The advantages of avoiding supervision are demonstrated by showing transfer of extreme poses, as well as between uncommon skeleton proportions.

关键词： pose transfer weak supervision Human skeleton

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human Behavior Recognition Method Based on Double-Branch Deep Convolution Neural Network

Human Behavior Recognition Method Based on Double-Branch Dee...

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第30届中国控制与决策会议

作者： Zhou Zhigang Duan Guangxue Lei Huan Zhou Guangbing Wang Nan Yang Wenjie School of Automation Guangdong University of Technology Guangdong Institute of Intelligent Manufacturing School of information Science and Engineering Shenyang University of Technology

Aiming at the poor robustness and the low accuracy of 2 D image recognition, this paper presents a method of human behaviors recognition based on double-branch deep convolution neural network. Firstly, the features of the input image are extracted, and the feature maps are input into the double-branch deep convolution neural network to obtain the joints information of human body and the joints connection information of human body respectively. And we used the Hopcroft-Karp algorithm to optimally match skeletal joints to obtain the human skeletal sequence diagram. The human behaviors are identified by the multi-classification support vector machines. Finally, through the training and testing of standing, walking, running, waving, bending, squatting and sitting on seven kinds of general human behaviors, the experimental results show that the proposed approach has good accuracy and robustness.

关键词： 2D image deep convolution neural network behavior recognition Human skeleton multi-classification support vector machines

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