Department of Electrical Engineering, Graduate University of advanced technology, Kerman
Abstract
Human hand movements are used in the non-verbal interaction between human and computers. Intuitive and natural hand movements is the most important factor motivating researchers to use the hands to improve the human-computerinteraction. In this paper, for hand gesture recognition, hand as the only moving object in the video is detectedusing the difference between frames. After that, hand movement feature vector is extracted. This vector is used to detect hand gesture using artificial neural network. Two methods are proposed for feature-vector extraction. The first method codes themotion trajectory of the final hand pixel in the frames. The second method uses two angle histograms. Identifying six different gestures with recognition rate of 95.54 percent using the first method and 91.53 percent using the second method, shows the efficiency of the proposed system. Also, the comparison between the proposed feature vectors with a conventional method shows the superiority of theproposed methods in terms of accuracy, the number of features, and classifier training time.
Azadipour ghahestani, F., & Rashedi, E. (2016). Hand gesture recognition for human computer interaction using motion trajectory coding and artificial neural network. Journal of Machine Vision and Image Processing, 3(2), 13-22.
MLA
Farzaneh Azadipour ghahestani; Esmat Rashedi. "Hand gesture recognition for human computer interaction using motion trajectory coding and artificial neural network". Journal of Machine Vision and Image Processing, 3, 2, 2016, 13-22.
HARVARD
Azadipour ghahestani, F., Rashedi, E. (2016). 'Hand gesture recognition for human computer interaction using motion trajectory coding and artificial neural network', Journal of Machine Vision and Image Processing, 3(2), pp. 13-22.
VANCOUVER
Azadipour ghahestani, F., Rashedi, E. Hand gesture recognition for human computer interaction using motion trajectory coding and artificial neural network. Journal of Machine Vision and Image Processing, 2016; 3(2): 13-22.
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