Human Pose Estimation Using Deep Convolutional Densenet Hourglass Network with Intermediate Points Voting

Human pose estimation is a long-standing and challenging problem in computer vision. The problem involves high freedom of articulation of body limbs, different occlusions such as self-occlusion or occlusion by other objects or persons, various clothing, various background in the natural image and foreshortening due to different capturing angle of the camera. In this work we present 1) how the DenseNet module can be used to improve the original ResNet hourglass model, 2) how intermediate points derived from ground truth joint segments can be used as output augmentation of a convolutional neural network (ConvNet) to improve the prediction accuracy. Further improvement has also been made via intermediate points voting by optimizing the joint probability distribution of human joints and the intermediate points. Experimental results on the effects of intermediate point and optimization scheme are presented. We are able to achieve competitive results to the state-of-the-art methods by the proposed method.