Mendeley
TY  _  JOUR
ID  -  13971227178302
TI  -  Human Activity Recognition using Switching  Structure Model
JO  -  Nashriyyah -i Muhandisi -i Barq va Muhandisi -i Kampyutar -i Iran
JA  -  ES
LA  -  fa
SN  -  16823745
AU  -  Arzani Mohammad Mahdi
AU  -  mahfathy mahfathy
AU  -  akbari ahmad
AD  -  دانشگاه علم و صنعت ایران
AD  -  
AD  -  
Y1  -  2020
PY  -  2020
VL  -  1
IS  -  
SP  -  1
EP  -  14
KW  -  Probabilistic graphical modelshuman activity recognitiondistributed structured predictionskeleton
DO  -  
N2  -  To communicate with people interactive systems often need to understand human activities in advance. However, recognizing activities in advance is a very challenging task, because people perform their activities in different ways, also, some activities are simple while others are complex and comprised of several smaller atomic sub-activities. In this paper, we use skeletons captured from low-cost depth RGB-D sensors as high-level descriptions of the human body. We propose a method capable of recognizing simple and complex human activities by formulating it as a structured prediction task using probabilistic graphical models (PGM). We test our method on three popular datasets: CAD-60, UT-Kinect, and Florence 3D. These datasets cover both simple and complex activities. Also, our method is sensitive to clustering methods that are used to determine the middle states, we evaluate test different clustering, methods.
UR  - rimag.ir/en/Article/28394
L1  - rimag.ir/en/Article/Download/28394
ER  -  
