Rapid development of software and hardware enables wide application of electronic computer in various aspects of life, which makes the study on Human-Computer Interaction practically and theoretically significant. Traditional tools of HCI, such as keyboard, mouse and graphic interface, cannot meet the demand of more and more diverse applications. Therefore, it is an inevitable trend to develop new model of interaction featured by simplicity, immediacy and user-friendliness. In this regard, vision-based hand gesture interaction technology satisfies current needs and is now extensively used in Digital Entertainment, Virtual Reality, Sign Language Recognition, Remote Control and other areas. In view of academic value of hand gesture based interaction technology and its prospect of wide application, this thesis aims to make an in-depth study on the hand detection, hand track and dynamic hand gesture recognition of real-time against natural background, and to work out a dynamic gesture recognition system. The thesis includes following main contents. 1. Adaboost, a widely used algorithm in the detection of human face, is employed to detect human hands. And the feature called Histogram of Orientation Gradient (HOG) is used to replace traditional Haar feature. Combination of HOG and efficient Adaboost ensures satisfactory effect of human hand detection. 2. Use the result of hand detection to initialize the hand tracker automatically. Construct a linear skin classifier from the optimal initialized hand contour. The skin classifier is highly individual-specific, it claims relatively fewer computational efforts but works out good effect. Moreover, it allows parameter adjustment based on skin extracting effect in later tracking stage, which makes it adaptive to changes of environment. 3. Real-time tracking of human hand contour with high degree of freedom is achieved by combining Condensation and a heuristic scanning method. Condensation is used to determine the rigid motion of the whole hand, and heuristic scanning method is used to find the angle and length of each finger. 4. Trajectory of human hand is recognized with Hidden Markov Chain (HMM). And a system combining hand detection, contour tracking and gesture recognition is built.
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