Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (6A): 220400199-7.doi: 10.11896/jsjkx.220400199

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Cross-dataset Learning Combining Multi-object Tracking and Human Pose Estimation

ZENG Zehua, LUO Huilan   

  1. School of Information Engineering,Jiangxi University of Science and Technology,Ganzhou,Jiangxi 341000,China
  • Online:2023-06-10 Published:2023-06-12
  • About author:ZENG Zehua,born in 1995,postgra-duate.His main research interests include multi-object tracking and human pose estimation. LUO Huilan,born in 1974,Ph.D,professor.Her main research interests include machine learning and pattern re-cognition.
  • Supported by:
    National Natural Science Foundation of China(61862031) and Jiangxi Provincial Foundation for Leaders of Disciplines in Science,Leading Talents Program(20213BCJ22004).

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Abstract: In recent years,multi-object tracking has gained significant progress,especially for pedestrians.By performing joint pose estimation on pedestrians,it is possible to improve the motion prediction of pedestrians by multi-object tracking algorithms,while providing more information for higher-order tasks such as autonomous driving.However,in the current multi-object tra-cking dataset containing human pose estimation labels,the video length is short and the targets are sparse,limits the research of multi-object tracking.In the paper,cross-dataset learning is performed using the multi-object tracking dataset MOT17 and the multi-human pose estimation dataset COCO with more pedestrians.The performance of the multi-object tracking algorithm under joint human pose estimation is effectively improved based on a round-robin training strategy.The use of simultaneous polarized self-attention down-sampling and attention up-sampling enhances the human pose estimation performance of the algorithm while improving the algorithm training speed.

Key words: Multi-object tracking, Cross-dataset learning, Human pose estimation, Attention mechanism

CLC Number: 

  • TP183
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