Pose Scoring Model for Refining Multi-Person Poses

In multi-person pose estimation, the coordinates of body joints are estimated to predict the body poses of several people in an image. Although significant progress has been made in this area in recent years, the solutions still suffer from certain weaknesses. One weakness is the detection of a larger number of poses than the actual number of human instances in the image. To face this problem, the final poses are often selected by thresholding the detected human scores. This, however, is not optimal because a pose with a lower priority may be assigned a higher score and selected as the final pose. In this paper, we introduce a problem, explain the reason, and present a pose scoring model as a solution. This model predicts the confidence score for the pose based on the human image, the location and the confidence score of the joints and the structure between the joints of the body. We formulate the problem using Conditional Random Fields and introduce suitable potential functions. To infer the best pose score, we design a neural network whose inputs are a human image and an initial pose. To train the model, a lot of training data and corresponding labels should be provided. For this purpose, we introduce Monte Carlo and GAN-based data augmentation methods, and a scoring function to train the model. Also, we investigate the effect of the pose scores on the reliability and accuracy of multi-person pose estimation models. The proposed idea is implemented as a deep neural network and can be easily applied to other multi-person pose estimation models as a model-agnostic method. In addition to the usual pose estimation evaluation metrics, we consider the number of poses to compare the models in terms of reliability. Experiments show that the performance of the proposed model is higher than the state-of-the-art models. Conducting further research can help to simplify and reduce the complexity of the model.