Appearance modeling for object pose recognition using canonical correlation analysis

This paper presents an appearance-based method for object pose recognition using single camera image. The basic idea of our method is to find the correlation between object image and its pose, and use it for object recognition and pose estimation. Canonical Correlation Analysis is introduced to derive such correlation and build a compact appearance model. In the approach, we first obtain a pair of training data set, i.e., object images and their pose parameters. The appearance model is given as the subspace spanned by the canonical vectors that maximize the correlation between images and poses. Pose parameters of currently observed image is predicted by finding the regression coefficient in this subspace. We also introduce the kernel methods to cope with the non-linearity lies in training data set. Experiments are conducted on object pose estimation and vehicle type classification problem. Performance of our appearance models is discussed through the comparison with conventional subspace method.