Real - Time Hand Gesture Recognition

As robots become more integrated into humans' everyday lives, it becomes essential for them to perceive and understand people's intentions and actions. People will want a more natural way to interact with robots, closer to the way humans do it among themselves. When humans communicate, they use a rich set of gestures and body language in addition to speech, which signiicantly enhances their ability to get thoughts, feelings, and intentions across. Hence, robots strongly need the ability to understand human gestures and to interpret body language. One of the most expressive components of body language are hand gestures, which is why they are so important at the interface between humans and robots. This thesis presents an extensible, vision-based communication system that is able to interpret 2D dynamic hand gestures. The system consists of three primary modules: hand segmentation, feature extraction , and gesture recognition. Hand segmentation uses both motion and skin-color detection to separate the hand from cluttered backgrounds. The feature extraction module gathers hand trajectory information and encapsulates it into a form that is used by the gesture recognizer. This last module identiies the gesture and translates it into a command that the robot can understand. Unlike other existing work, this hand gesture recognition system operates in real-time without the use of special hardware. Results demonstrate that the system is robust in realistic, complex environments. Moreover, this system does not require a priori training and allows the hand great exibility of motion without the use of gloves or special markers. First and foremost, I would like to thank my parents, sisters, and grandmother for all the support and love they have always given me. Their strong spirit and conndence in me have been a great inspiration to me. I would also like to thank Rod for giving me the opportunity to work in a eld I used to only dream of. His endless support, and guidance have made my past two years much more memorable. Special thanks to Scaz for all the helpful discussions and suggestions he has ooered me and for doing careful revisions of my thesis. My thanks to all the members of the Yuppy and Cog group, especially Junji, and Charlie for all the useful ideas they have provided to my work and for making my experience at the AI lab much more enjoyable. Also, thanks to Chandana and all the people who helped me test …