Super Resolution in Human Pose Estimation: Pixelated Poses to a Resolution Result?
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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 low resolution people their keypoint detection performance improved once SR was applied. Second, the keypoint detection performance gained is dependent on the persons initial resolution (segmentation area in pixels) in the original image; 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 for each of our HPE performance metrics.
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