Regression Based Pose Estimation with Automatic Occlusion Detection and Rectification

Human pose estimation is a classic problem in computer vision. Statistical models based on part-based modelling and the pictorial structure framework have been widely used recently for articulated human pose estimation. However, the performance of these models has been limited due to the presence of self-occlusion. This paper presents a learning-based framework to automatically detect and recover self-occluded body parts. We learn two different models: one for detecting occluded parts in the upper body and another one for the lower body. To solve the key problem of knowing which parts are occluded, we construct Gaussian Process Regression (GPR) models to learn the parameters of the occluded body parts from their corresponding ground truth parameters. Using these models, the pictorial structure of the occluded parts in unseen images is automatically rectified. The proposed framework outperforms a state-of-the-art pictorial structure approach for human pose estimation on 3 different datasets.