Abstract
In this paper, a real-time dynamic hand gesture recognition system with gesture spotting function is proposed. In the proposed system, input video frames are converted to feature vectors, and they are used to form a posture sequence vector that represents the input gesture. Then, gesture identification and gesture spotting are carried out in the self-organizing map (SOM)-Hebb classifier. The gesture spotting function detects the end of the gesture by using the vector distance between the posture sequence vector and the winner neuron’s weight vector. The proposed gesture recognition method was tested by simulation and real-time gesture recognition experiment. Results revealed that the system could recognize nine types of gesture with an accuracy of 96.6%, and it successfully outputted the recognition result at the end of gesture using the spotting result.
Highlights
Recognition System with GestureHand gestures are one of the most important communication tools frequently used in our daily lives, and they can be used as an attractive means of human–computer interaction (HCI)
Hand signs are static hand poses without any movements, and the hand gesture is defined as dynamic movement, which is a sequence of hand poses
This paper proposes a new video-based dynamic hand gesture recognition system with the gesture spotting
Summary
Recognition System with GestureHand gestures are one of the most important communication tools frequently used in our daily lives, and they can be used as an attractive means of human–computer interaction (HCI). Hand signs are static hand poses without any movements, and the hand gesture is defined as dynamic movement, which is a sequence of hand poses. In the dynamic gesture recognition, each gesture is defined as the trajectory of the hand movement or a sequence of hand poses. A number of video-based hand gesture recognition algorithm and systems have been proposed [1]. This approach can use a conventional camera that most laptop PCs are equipped with. The video-based gesture recognition system can be implemented on widely available platforms. Another approach is based on three-dimensional hand image, which has attracted researchers in gesture recognition because the use of
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