Human pose estimation in low-resolution images

Abstract: This project explores the understudied, yet important, case of human pose estimation in low-resolution images. This is done in the use-case of images with football players of known scale in the image. Human pose estimation can mainly be done in two different ways, the bottom-up method and the top-down method. This project explores the bottom-up method, which first finds body keypoints and then groups them to get the person, or persons, within the image. This method is generally faster and has been shown to have an advantage when there is occlusion or crowded scenes, but suffers from false positive errors. Low-resolution makes human pose estimation harder, due to the decreased information that can be extracted. Furthermore, the output heatmap risks becoming too small to correctly locate the keypoints. However, low-resolution human pose estimation is needed in many cases, if the camera has a low-resolution sensor or the person occupies a small portion of the image. Several neural networks are evaluated and, in conclusion, there are multiple ways to improve the current state of the art network HigherHRNet for lower resolution human pose estimation. Maintaining large feature maps through the network turns out to be crucial for low-resolution images and can be achieved by modifying the feature extractor in HigherHRNet. Furthermore, as the resolution decreases, the need for sub-pixel accuracy grows. To improve this, various heatmap encoding-decoding methods are investigated, and by using unbiased data processing, both heatmap encoding-decoding and coordinate system transformation can be improved.