Human Body-Aware Feature Extractor Using Attachable Feature Corrector for Human Pose Estimation

Top-down pose estimation generally employs a person detector and estimates the keypoints of the detected person. This method assumes that only a single person exists within the bounding box cropped by detection. However, this assumption leads to some challenges in practice. First, a loose-fitted bounding box may include certain body parts of a non-target person. Second, spatial interference between several people exists owing to occlusion, so more than a single person can exist in the cropped image. In such scenarios, the pose estimation may falsely predict the keypoints of two or more persons as those of a single person. To tackle these issues, this paper proposes the human body-aware feature extractor based on the global- and local-reasoning features. The global-reasoning feature considers the entire body using transformer's non-local computation property and the local-reasoning feature concentrates on the individual body parts using convolutional neural networks. With those two features, we extract corrected features by filtering unnecessary features and supplementing necessary features using our proposed novel architecture. Hence, the proposed method can focus on the target person's keypoints, thereby mitigating the aforementioned concerns. Our method achieves noticeable improvement when applied to state-of-the-art top-down pose estimation networks.