  The results obtained from state of the art human pose estimation (HPE) models
degrade rapidly when evaluating people of a low resolution, but can super
resolution (SR) be used to help mitigate this effect? By using various SR
approaches we enhanced two low resolution datasets and evaluated the change in
performance of both an object and keypoint detector as well as end-to-end HPE
results. We remark the following observations. First we find that for people
who were originally depicted at a low resolution (segmentation area in pixels),
their keypoint detection performance would improve once SR was applied. Second,
the keypoint detection performance gained is dependent on that persons pixel
count in the original image prior to any application of SR; keypoint detection
performance was improved when SR was applied to people with a small initial
segmentation area, but degrades as this becomes larger. To address this we
introduced a novel Mask-RCNN approach, utilising a segmentation area threshold
to decide when to use SR during the keypoint detection step. This approach
achieved the best results on our low resolution datasets for each HPE
performance metrics.
