Human Behavior Analysis Using Multiple Features and AdaBoosting

• Main Authors: Wei-chiau Li, 李偉僑
• Other Authors: Jin-jang Leou
• Format: Others
• Language: en_US
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/41181196990445088723

碩士 === 國立中正大學 === 資訊工程所 === 96 === Most human behavior systems nowadays use single feature or single classifier algorithm to do human behavior analysis. But if we use only one feature to analyze human behavior, many analysis can’t be do well in many case. For example, if we only use shape silhouette information to do human behavior analysis, the change information between frames in continue time doesn’t be considered in analysis procedure. And we also can’t realize what is the best analysis algorithm in some specific environment. In this paper, we use AdaBoosting based algorithm, with multiple features and different classifier, to combine all of them, and to create more reliable analysis results. For using AdaBoosting, one the restriction is all classifier must be weak. In this paper, we unanalyzed some actions deliberately to create many weak classifiers from original classifier. Finally, we combine all of them using AdaBoosting. The accuracy rate of these classifier is much lower than general classifiers, but after combing them by AdaBoosting algorithm, we will find that the better analysis results can be get in the experiments.