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Can Super Resolution be used to improve Human Pose Estimation in Low Resolution Scenarios?

arXiv 2107.02108 ↗First posted 5 Jul 2021cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 24 of its 40 references that are in the map. First posted on arXiv 5 Jul 2021.

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24 of 40 references.

References · 24

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  2. 2017Mask R-CNNcited by 7,259
  3. 2016Feature Pyramid Networks for Object Detectioncited by 5,021
  4. 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,898
  5. 2019Deep High-Resolution Representation Learning for Visual Recognitioncited by 1,417
  6. 2019Deep High-Resolution Representation Learning for Human Pose Estimationcited by 1,339
  7. 2018ESRGAN: Enhanced Super-Resolution Generative Adversarial Networkscited by 1,250
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  11. 2017Cascaded Pyramid Network for Multi-Person Pose Estimationcited by 341
  12. 2013DeepPose: Human Pose Estimation via Deep Neural Networkscited by 305
12 more
  1. 2020Deep Unfolding Network for Image Super-Resolutioncited by 253
  2. 2019HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimationcited by 247
  3. 2020Whole-Body Human Pose Estimation in the Wildcited by 178
  4. 2014Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimationcited by 129
  5. 2020Monocular Human Pose Estimation: A Survey of Deep Learning-based Methodscited by 59
  6. 2017Learning Feature Pyramids for Human Pose Estimationcited by 57
  7. 2018The Effects of Super-Resolution on Object Detection Performance in Satellite Imagerycited by 57
  8. 2016Human pose estimation via Convolutional Part Heatmap Regressioncited by 51
  9. 2019Multi-task Deep Learning for Real-Time 3D Human Pose Estimation and Action Recognitioncited by 35
  10. 2017Simultaneous Super-Resolution and Cross-Modality Synthesis of 3D Medical Images using Weakly-Supervised Joint Convolutional Sparse Codingcited by 33
  11. 2016Learning scale-variant and scale-invariant features for deep image classificationcited by 20
  12. 2020Feature-Driven Super-Resolution for Object Detectioncited by 1

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