Image Based Real-Time Hand Gesture Recognition System Design
|Keywords:||類神經網路;手勢辨識;手臂去除;Neural Network;Hand Gesture Recognition;Arm Cropped|
This thesis focuses on the development of real-time hand gestures recognition system. There are six hand gestures, including turn left, turn right, upward, downward, horizontal swing and vertical swing. The system operates in four stages, which are object tracking, object image pre-processing, feature extraction and hand gesture classification. During the operation, the hand is always traced by the mean-shift tracking algorithm. Then, the hand image, with the arm cropped, is further extracted from the background in the pre-processing stage. After that, the lattice average and the scale and translation invariant moment are calculated in the feature extraction stage to form a feature vector, which will be classified into some finite states by the K-means algorithm. However, two gestures with the same meaning may be represented by different number of states since some of the states are repeated or generated transiently. In order to deal with such problem, a sequence of neural networks is developed to eliminate the repeat and transient states. Finally, the resulted state sequence is fed into the hand gesture classification neural network. From the experimental results, the proposed hand gesture recognition system can perform in real-time and possess good recognition rates.
|Appears in Collections:||Thesis|