| Summary: | 碩士 === 國立臺灣大學 === 電機工程學研究所 === 99 === In this thesis, an indoor human actions recognition system is proposed. Generally, actions are composed by sequences of key postures. In our approach, the order of key postures is not considered, that is, only the compositions of actions are considered. The center point trajectory of the target is analyzed to substitute for the order of key postures.
By using pattern matching process, the input human silhouette is matched with the key postures pre-stored in the database, and some key postures are matched in each frame. The idea of our approach is that people associate the possible actions when seeing a key posture. In other words, each key posture is related to one or more actions. Taking this idea, every key posture in database has weights for all actions, called weighting vector. The weighting vectors of matched key postures in recent frames give action scores for every action. Because this method has no property of the order of key postures, the center point trajectory is analyzed to distinguish actions with same key postures but have different orders.
The feature of our approach is that the order of key postures is not utilized. The composition of key postures and center point trajectory are used to recognize human actions. This method is robust against the error result of pattern matching, and the stationary temporal situations are also considered. Besides, in pattern matching process, a feature called “distance vector” [41] which was applied in gait recognition is modified and utilized as the pattern feature. This feature keeps the characteristic of human exterior contours well. Seventeen common human actions and static postures are recognized, and 5 subjects are tested in 4 viewpoints. The recognition rate of our approach is 89.23%, and the experiment result shows that our approach has potential to recognize more actions.
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