Electronic Science and Technology ›› 2020, Vol. 33 ›› Issue (12): 17-21.doi: 10.16180/j.cnki.issn1007-7820.2020.12.004

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Research on Dynamic Gesture Recognition Based on Dense Trajectories Features

WANG Hehe,LI Feifei,CHEN Qiu   

  1. School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093, China
  • Received:2019-09-19 Online:2020-12-15 Published:2020-12-22
  • Supported by:
    Program for Professor of Special Appointment(Eastern Scholar) at Shanghai Institutions of Higher Learning(ES2015XX)


In view of at the low performance of feature point detection for gesture recognition, the paper propose a new edge feature point detection method. By using the feature description method of the dense trajectories method, the method of support vector machine classification learning is uses to realize dynamic gesture recognition. This method increases the number of edge trajectories effectively, which brings benefits to the final recognition result. The method is evaluated in Cambridge University Gesture Dataset and Sheffield Gesture Dataset, and 99.11% and 99.72% gesture recognition accuracy is obtained respectively, which embodies the excellent performance of the algorithm in the above datasets.

Key words: gesture recognition, edge feature, dense optical flow, edge trajectories, feature reduction, Fisher vector, support vector machine

CLC Number: 

  • TP391