| Summary: | 碩士 === 國立交通大學 === 電控工程研究所 === 105 === Vision-based gesture recognition is an important and active field of computer vision research due to its wide applications. This thesis develops a one-shot-learning gesture recognition system which is based on Histogram of Oriented Gradients (HOG), Histogram of Optical Flow (HOF), and Dynamic Time Warping (DTW). The system is composed of three parts, including preprocessing, feature extraction, and gesture recognition. The preprocessing algorithms are proposed for both the RGB videos and the depth videos. For the RGB videos, they are preprocessed by contrast stretching and downsampling, while for the depth videos, they are preprocessed by inpainting, median filter, and contrast stretching. As for the feature extraction, HOG and HOF are respectively extracted from the depth videos and the RGB videos. Besides, a weight function is designed for Lucas-Kanade optical flow model to obtain a proper estimation of optical flow. Finally, DTW with Quadratic-Chi distance is adopted to execute gesture recognition, and temporal segmentation is simultaneously performed. The experiment results show that the proposed system has a better performance when compared to some other approaches applied to the same database ChaLearn Gesture Dataset 2011.
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