Enabling High-Resolution Pose Estimation in Real Time Using Active Perception

Deep Learning (DL) models have enabled very accurate pose estimation. However, most of the existing approaches require images of relatively high resolution, since locating body parts and joints accurately is challenging, which increases the computational cost of these approaches. To overcome this limitation in this paper we propose an active perception method for high-resolution pose estimation that enables efficiently selecting the most appropriate image region for analysis and then employing a bottom-up pose estimator on the corresponding region. This allows for significantly improving the efficiency of pose estimation by selectively analyzing in high resolution only the parts of the image that contain humans. To ensure the computational efficiency of the proposed method we propose using low-resolution heat maps extracted using the same pose estimation model in order to guide the active perception process. The proposed method is model agnostic since it can be combined with any bottom-up pose estimation model in order to enable high-resolution analysis. We have experimentally evaluated the proposed method using a well-known pose estimation model, Lightweight OpenPose, demonstrating its effectiveness on three high-resolution variants of the COCO2017 dataset.