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基于深度序列的时空金字塔的动作识别
引用本文:邹向阳,侯云江. 基于深度序列的时空金字塔的动作识别[J]. 计算机工程与应用, 2017, 53(19): 211-215. DOI: 10.3778/j.issn.1002-8331.1604-0136
作者姓名:邹向阳  侯云江
作者单位:1.空军空降兵学院,广西 桂林 5410032.桂林电子科技大学 电子工程与自动化学院,广西 桂林 541004
摘    要:提出一种高效的人体动作识别方法。通过帧间差分法将深度序列的三视图转化为深度运动轮廓序列(DMOS),然后利用时空金字塔对DMOS进行时间维和空间维细分,将细分后得到的空间网格的局部方向梯度直方图(HOG)进行特征融合,并使用线性SVM分类。最后采用MSR Action 3D数据集对提出的算法在不同时空金字塔参数下的识别率和处理速度进行了评估,结果表明该方法在同类算法中具有更高的识别率。

关 键 词:动作识别  深度运动轮廓序列  时空金字塔  HOG特征  线性SVM分类器  

Spatio-temporal pyramid for action recognition based on depth sequences
ZOU Xiangyang,HOU Yunjiang. Spatio-temporal pyramid for action recognition based on depth sequences[J]. Computer Engineering and Applications, 2017, 53(19): 211-215. DOI: 10.3778/j.issn.1002-8331.1604-0136
Authors:ZOU Xiangyang  HOU Yunjiang
Affiliation:1.Air Force Airborne Academy, Guilin, Guangxi 541003, China2.College of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin, Guangxi 541004, China
Abstract:An efficient human action recognition method is put forward. Three views of depth sequences are transformed into Depth Motion Outline Sequence(DMOS) by using the method of interframe differentiation. Then a spatio-temporal pyramid is proposed to subdivide the DMOS on temporal and spatial level. A feature fusion scheme is presented to concatenate the Histograms of Oriented Gradients(HOG) features which have extracted from the subdivided DMOS. Finally linear SVM to classification is used. Through using the MSR Action 3D data sets, this method is evaluated with different parameters of spatial-temporal pyramid. Experimental results show that this method has higher recognition rate than the similar algorithm.
Keywords:action recognition  Depth Motion Outline Sequence(DMOS)  spatio-temporal pyramid  Histogram of Oriented Gradient(HOG)  linear SVM classifier  
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