Computer Science ›› 2021, Vol. 48 ›› Issue (8): 334-339.doi: 10.11896/jsjkx.201000036

• Human-Machine Interaction • Previous Articles    

Interactive Group Discovery Based on Skeleton Trajectory Aggregation Model in ClassEnvironment

GAO Yan, YAN Qiu-yan, XIA Shi-xiong, ZHANG Zi-han   

  1. School of Computer Science & Technology,China University of Mining and Technology,Xuzhou,Jiangsu 221116,China
  • Received:2020-10-09 Revised:2020-11-08 Published:2021-08-10
  • About author:GAO Yan,born in 1997,postgraduate,is a member of China Computer Federation.Her main research interests include education big data analysis and time series data mining.( Qiu-yan,born in 1978,Ph.D,associate professor,is a member of China Computer Federation.Her main research interests include multi-modal image behavior recognition,education big data analysis and time series data mining.
  • Supported by:
    National Natural Science Foundation of China(61977061,51934007).

Abstract: Traditional class action recognition methods focus on the recognition of interactive behavior itself,not the group discovery.Accurate positioning and discovery of interactive groups in class environment is the basis for further individual behavior recognition,but there is a problem of missing behavior data caused by occlusion.Skeletal data represent human behavior and motion trajectory,and have the advantages of not being disturbed by light and background,and simple data expression.Aiming at multi-person interactive group discovery of skeleton data,an interactive group discovery algorithm based on skeleton trajectory aggregation (IGSTA) is proposed.Firstly,this paper standardizes the skeleton data into the human-centered coordinate system to reduce the impact on recognition accuracy of different body size and initial position of person.Secondly,a skeleton trajectory aggregation model based on multi-core representation is proposed to accurately describe the changes of students' interactive beha-vior groups.Finally,the aggregated skeleton trajectories are clustered to realize the discovery of interactive group.Kinect is used to obtain the simulated video of classroom student interaction behavior.Experiments proves the validity of the method,that is,in the case of missing skeleton nodes,the interaction group of students can be accurately found in class environment.

Key words: Class interaction, Interactive group, Skeleton data, Trajectory clustering

CLC Number: 

  • TP391
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