DocumentCode
719175
Title
Histograms of orientation gradient investigation for static hand gestures
Author
Sheenu ; Joshi, Garima ; Vig, Renu
Author_Institution
ECE Dept., Panjab Univ., Chandigarh, India
fYear
2015
fDate
15-16 May 2015
Firstpage
1100
Lastpage
1103
Abstract
In this paper, Histograms of Orientation Gradient (HOG) algorithm is used to identify the static hand gestures. Experimental results show that HOG descriptor is a better shape descriptor than existing feature sets for gesture recognition. The overall algorithm has only three main steps; pre-processing, feature extraction and classification. It completely omits the segmentation phase. SVM is used for recognition of gestures. High recognition accuracy is achieved for 11 hand gestures.
Keywords
feature extraction; gesture recognition; image classification; support vector machines; HOG algorithm; HOG descriptor; SVM; classification; feature extraction; gesture recognition; histogram of orientation gradient investigation; preprocessing; shape descriptor; static hand gestures; Accuracy; Feature extraction; Gesture recognition; Histograms; Image color analysis; Shape; Support vector machines; Histograms of Oriention Gradient; feature extraction; hand gesture; human computer interaction;
fLanguage
English
Publisher
ieee
Conference_Titel
Computing, Communication & Automation (ICCCA), 2015 International Conference on
Conference_Location
Noida
Print_ISBN
978-1-4799-8889-1
Type
conf
DOI
10.1109/CCAA.2015.7148539
Filename
7148539
Link To Document