Analysis of PCA based feature vectors for SVM posture classification

Many classifiers have been employed to classify human posture classification; however, most of them only presents the average accuracy of the classification. Furthermore, the details of the measured parameters especially for SVM classifier are not measured. Therefore, the objective of this work is to analyse and classify human body posture using Support Vector Machine (SVM) techniques based on various two combinations of eigenpostures by considering two different solvers in the training process. The two solvers namely Sequential Minimal Optimization (SMO) and Matlab Quadratics Programming (QP) solvers have been studied and analyzed to perform the SVM training. The principal component analysis (PCA) method is applied to extract the features from human shape silhouettes. These extracted feature vectors are then used to perform human posture classification. Human posture evaluates which eigenpostures (feature vectors of the several eigenvalues) can be used to classify either human standing posture or human non-standing posture. Next, the solvers that produced the best performance in classifying human postures as well as the best combination of eigenpostures were selected. The results verified that the combination of second and fourth eigenpostures gives the superb performance with 100% correct classification and it is shown that the best solver in training process to classify human body posture classification is the SMO based on the shortest CPU time attained.