基于稠密轨迹特征的动态手势识别研究

doi:10.16180/j.cnki.issn1007-7820.2020.12.004

Abstract

Abstract:

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

WANG Hehe,LI Feifei,CHEN Qiu. Research on Dynamic Gesture Recognition Based on Dense Trajectories Features[J].Electronic Science and Technology, 2020, 33(12): 17-21.

Figures/Tables 7

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References 33

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数据集	类别/类	视频数/个	训练集/个	测试集/个
剑桥大学	9	900	450	225
谢菲尔德	10	1 080	720	360

方法	精度/%
文献[26]	93.30
文献[27]	93.88
文献[28]	97.31
文献[29]	98.70
本文	99.72

方法	精度/%
文献[30]	93.61
文献[31]	96.00
文献[32]	97.78
文献[33]	98.23
本文	99.11

Research on Dynamic Gesture Recognition Based on Dense Trajectories Features

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