Mendeley
TY  _  JOUR
ID  -  13981126225426
TI  -  Human Activity Recognition based on Deep Belief Network Classifier and Combination of Local and Global Features
JO  -  Journal of Information Systems and Telecommunication (JIST) 
JA  -  ES
LA  -  en
SN  -  2322-1437
AU  -  Mahmoodzadeh Azar
AD  -  Islamic Azad University Shiraz
Y1  -  2021
PY  -  2021
VL  -  33
IS  -  9
SP  -  45
EP  -  54
KW  -  BoW
KW  -   DBN
KW  -   GIST
KW  -   HOG
KW  -   Human Activity Recognition
KW  -   SIFT
DO  -  
N2  -  During the past decades, recognition of human activities has attracted the attention of numerous researches due to its outstanding applications including smart houses, health-care and monitoring the private and public places. Applying to the video frames, this paper proposes a hybrid method which combines the features extracted from the images using the ‘scale-invariant features transform’ (SIFT), ‘histogram of oriented gradient’ (HOG) and ‘global invariant features transform’ (GIST) descriptors and classifies the activities by means of the deep belief network (DBN). First, in order to avoid ineffective features, a pre-processing course is performed on any image in the dataset. Then, the mentioned descriptors extract several features from the image. Due to the problems of working with a large number of features, a small and distinguishing feature set is produced using the bag of words (BoW) technique. Finally, these reduced features are given to a deep belief network in order to recognize the human activities. Comparing the simulation results of the proposed approach with some other existing methods applied to the standard PASCAL VOC Challenge 2010 database with nine different activities demonstrates an improvement in the accuracy, precision and recall measures (reaching 96.39%, 85.77% and 86.72% respectively) for the approach of this work with respect to the other compared ones in the human activity recognition.
UR  - rimag.ir/fa/Article/15439
L1  - rimag.ir/fa/Article/Download/15439
ER  -  
