Object Localization Algorithm Based on Mixture Model of HOG Feature and LSVM

Because the traditional object localization algorithm based on histogram of oriented gradient (HOG) feature is difficult to detect the object when the target poses are complex. A localization algorithm of Object-of-Interest (OOI) based on mixture model of HOG feature and LSVM to solve this problem is presented. Firstly, the features of HOG for mixture models of OOI that include root models, part models and corresponding deformation models are computed. And then, the classifiers LSVM for mixture models of OOI that include root models, part models and corresponding deformation models are trained effectively by HOG feature pyramid of train images. Last, the OOI is localized according to dynamic programming and generalized distance transforms under which the matching region with the deformation models in test images. The models capture not only general outline of the targets, but also more specific target parts outline, so it is robust when target poses are complex. The experimental results show that this method can solve the problem of localization when the target poses are complex such as partly changed and occluded.