StrongPose: Bottom-up and Strong Keypoint Heat Map Based Pose Estimation

The adaptation of deep convolutional neural network has made revolutionary advances in human body posture estimation. Various applications utilizing deep neural network for pose estimation have drawn considerable attention in recent years. However, prediction and localization of keypoints in single-person and multi-person images is still a challenging problem. Towards this, we propose a bottom-up approach to pose estimation and motion recognition. We present StrongPose system that deals with object-part associations using part-based modeling. The convolution network in our model detects strong keypoint heat maps and predicts their comparative displacements, allowing keypoints to be grouped into human instances. Further, it utilizes the keypoints to generate body heat maps that can determine the position of the human body in the image. The StrongPose system is based on fully convolutional engineering and makes proficient inferences while maintaining runtime regardless of the number of individuals in the image. We train and test the StrongPose on the COCO dataset. Evaluation results show that our framework achieves average precision of 0.708 using ResNet-101 and 0.725 using ResNet-152. Our results considerably outperform prior bottom-up frameworks.