| Summary: | 碩士 === 國立交通大學 === 電機與控制工程系所 === 96 === The main purpose of this thesis is to design of intelligent motion description system. This system can recognize different motion event according to cognitive motion state of human brain. There are three motion event including “Picking object”, “Putting object” and “Dropping object” which can be described by this system. The cognition of motion state and recognition of motion event can be learned by neural network. A motion event analyzer composed of trigger net and motion classifier applied to recognition of motion event. The motion event is closely related to change of motion state; therefore, a trigger net, applied here, used to turn motion state sequence into triggered state sequence then the triggered state sequence can be classified by motion classifier. Motion classifier can be implemented by two types of neural network, feed-forward and recurrent. However, feed-forward classifier is interfered by undefined motion state sequence. Therefore, undefined motion state sequence will be taken into account to prevent misclassification in design of feed-forward classifier. The recurrent classifier learns transient pattern according to different triggered state condition. Finally, the two types of motion classifier can reject the interference caused by undefined motion state sequence and reach better performance for recognition of motion event.
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