Leveraging active perception for real-time high-resolution pose estimation

The evolution of computational intelligence, especially deep learning, has revolutionized problem-solving approaches, with human pose estimation emerging as a popular challenge that relies on RGB images captured by cameras. Advancements in hardware have made high-resolution cameras more accessible and affordable, enabling their use across a variety of applications. Although deep learning models could theoretically benefit from the increased details provided by these high-resolution images, there is a trade-off between the resolution increase and inference time. To address this challenge, an efficient approach to high-resolution image analysis has been developed for pose estimation. This method selectively focuses on key regions within an image, allowing the pose estimation model itself to identify areas of interest without requiring a separate person detection model. By leveraging features from the model's backbone, it actively identifies important regions, enhancing both efficiency and accuracy. Moreover, a sequential processing approach refines the model's focus in stages, enabling it to retain high-resolution details that might be lost with traditional downscaling. This model-agnostic technique is adaptable across various pose estimation models, offering a flexible and computationally efficient solution.