Multi-level Prediction with Graphical Model for Human Pose Estimation.

More and more complex Deep Neural Networks (DNNs) are designed for the improvement of human pose estimation task. However, it is still hard to handle the inherent ambiguities due to diversity of postures and occlusions. And it is difficult to meet the requirements for the high accuracy of human pose estimation in practical applications. In this paper, reasoning-based multi-level predictions with graphical model for single person human pose estimation is proposed to obtain the accurate location of body joints. Specifically, a multi-level prediction using cascaded network is designed with recursive prediction according to three different levels from easy to hard joints. At each stage, multi-scale fusion and channel-wise feature enhancement are employed for stronger contextual information to improve capacity of feature extraction. Heatmaps with rich spatial and semantic information are refined by explicitly constructing graphical model to learn the structure information for inference, which can implement the interactions between joints. The proposed method is evaluated on LSP dataset. The experiments show that it can achieve highly accurate results and outperform state-of-the-art methods.