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This paper presents a human action recognition system based on motion capture features and support vector machine (SVM). We use 43 optical markers distributing on body and extremities to track the movement of human actions. In our system 21 different types of action are recognized. Applying SVM for the recognition of human action the overall prediction accuracy achieves to 84.1% when using the three-fold...
The effect of PC (principal component) number upon SAR ATR (synthetic aperture radar automatic target recognition) performance based on PCA (principal component analysis) is analyzed. First, PCA features are extracted with different PC number, and then SVM is used to classify. Experimental results based on MSTAR data sets show that the performance is optimized when the accumulative contribution rate...
In this paper, Adaboost and SVM are applied to SAR ATR (synthetic aperture radar automatic target recognition) respectively. The performance of these two classifiers is analyzed and compared in target aspect window with different size. First, PCA (principal component analysis) features are selected as target feature, and then Adaboost.Ml and SVM are used to classify, respectively. Experimental results...
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