DocumentCode :
2160628
Title :
A hidden Markov model based dynamic hand gesture recognition system using OpenCV
Author :
Shrivastava, Rudraksh
Author_Institution :
Dept. of Electron. & Commun. Eng., Maulana Azad Nat. Inst. of Technol., Bhopal, India
fYear :
2013
fDate :
22-23 Feb. 2013
Firstpage :
947
Lastpage :
950
Abstract :
In this paper we propose a novel and faster system for dynamic hand gesture recognition by using Intel´s image processing library OpenCV. Many hand gesture recognition methods using visual analysis have been proposed: syntactical analysis, neural networks, the hidden Markov model (HMM). In our research, a HMM is proposed for hand gesture recognition. The whole system is divided into three stages detection and tracking, feature extraction and training and recognition. The first stage uses a more non-conventional approach of application of Lαβ colour space for hand detection. While the process of features extraction is the combination of Hu invariant moments and hand orientation. For the training, Baum-Welch algorithm using Left-Right Banded (LRB) topology is applied and recognition is achieved by Forward algorithm with an average recognition rate above 90% for isolated hand gestures. Because of the use of OpenCV´s inbuilt functions, the system is easy to develop, its recognition rate is quite fast and so the system can be practically used for real-time applications.
Keywords :
feature extraction; gesture recognition; hidden Markov models; image colour analysis; learning (artificial intelligence); object detection; Baum-Welch algorithm; HMM; Hu invariant moment; LRB topology; OpenCV image processing library; colour space; detection-and-tracking stage; dynamic hand gesture recognition system; feature extraction; feature extraction stage; forward algorithm; hand detection; hand orientation; hidden Markov model; left-right banded topology; recognition rate; training-and-recognition stage; visual analysis; Computational modeling; Feature extraction; Gesture recognition; Hidden Markov models; Image color analysis; Training; Vectors; Hand Gesture Recognition; Hidden Markov Model (HMM); Hu Invariant Moments; Lαβ colour space; OpenCV;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advance Computing Conference (IACC), 2013 IEEE 3rd International
Conference_Location :
Ghaziabad
Print_ISBN :
978-1-4673-4527-9
Type :
conf
DOI :
10.1109/IAdCC.2013.6514354
Filename :
6514354
Link To Document :
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