A strong bilayer appearance model for human pose estimation from a high freedom still image

Appearance model is widely used for image description and demonstrates an impressive performance in object detection. However, most appearance models can not be applied to more freedom object in still image, especially when dealt with variant objects whose shapes are modified by warping, rotation, etc. In this article, a simple but effective method to build a regional rotation-invariant feature descriptor is proposed to catch discriminative information of the variant human pose, which has a superior advantage when targets are in arbitrary orientations and slightly warping. Moreover, a mixture spatial model with visible parameters is then presented to differentiate the body structure and estimate the visible accurate position of each joint. The experiment results indicate that the proposed descriptor give near state-of-the-art performance on both handwritten digit recognition database and two public human motion databases containing athletes or pedestrians under certain different variations.