Relevant Feature Selection for Human Pose Estimation and Localization in Cluttered Images

We address the problem of estimating human body pose from a single image with cluttered background. We train multiple local linear regressors for estimating the 3D pose from a feature vector of gradient orientation histograms. Each linear regressor is capable of selecting relevant components of the feature vector depending on pose by training it on a pose cluster which is a subset of the training samples with similar pose. For discriminating the pose clusters, we use kernel Support Vector Machines (SVM) with pose-dependent feature selection. We achieve feature selection for kernel SVMs by estimating scale parameters of RBF kernel through minimization of the radius/margin bound, which is an upper bound of the expected generalization error, with efficient gradient descent. Human detection is also possible with these SVMs. Quantitative experiments show the effectiveness of pose-dependent feature selection to both human detection and pose estimation.