Computer Science ›› 2026, Vol. 53 ›› Issue (8): 191-200.doi: 10.11896/jsjkx.251200195

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Teacher Trajectory Recognition and Reconstruction in Smart Classrooms via Multi-source Fusion

WANG Yifan1,2, YANG Peixuan2, LU Yuansuo3, LIU Mengjun2,3   

  1. 1 Hubei Academy of Educational Sciences, Wuhan 430079, China
    2 College of Teacher Education, Hubei University, Wuhan 430062, China
    3 School of Computer Science, Hubei University, Wuhan 430062, China
  • Received:2025-12-31 Revised:2026-04-16 Online:2026-08-15 Published:2026-08-17
  • About author:WANG Yifan,born in 1973,Ph.D,professor,Ph.D supervisor.His main research interests include education big data and educational assessment.
    LU Yuansuo,born in 2001,postgra-duate.His main research interests include machine vision and multimodal data fusion.
  • Supported by:
    Phased Research Achievements of the Key Project of National Education Science Planning(Ministry of Education) and 2021:Practical Research on Empowering Regional Education Quality Evaluation with Big Data Intelligent Collection Terminals(DHA210338).

Abstract: Teacher trajectory reconstruction is fundamental for modeling the spatio-temporal characteristics of teaching activities in video-based classroom behavior analysis.However,real classrooms involve frequent occlusions,viewpoint variations,and complex interactions,which make single-camera tracking methods struggle to maintain trajectory continuity and stability.To address these challenges,this paper proposes a multi-camera teacher trajectory tracking and reconstruction approach for smart classroom scenarios.By integrating multi-source observations,unified ground-plane mapping,and cross-view constraints,the proposed methodenables robust cross-view association and continuous reconstruction of multi-view trajectories.Moreover,it establishes an eva-luation framework focusing on trajectory continuity,interruption frequency,and motion smoothness to comparatively analyze different methods under occlusion and viewpoint transition conditions.Experimental results show that the proposed approach effectively reduces trajectory interruptions in complex dynamic scenes and achieves better trajectory continuity and motion smoothness,demonstrating its effectiveness and robustness for teacher trajectory reconstruction in real smart classroom environments.

Key words: Multi-camera tracking, Teacher trajectory, Trajectory reconstruction, Trajectory continuity, Motion smoothness

CLC Number: 

  • TP391.41
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