Benchmarking and Error Diagnosis in Multi-instance Pose Estimation

@article{Ronchi2017BenchmarkingAE,
  title={Benchmarking and Error Diagnosis in Multi-instance Pose Estimation},
  author={Matteo Ruggero Ronchi and Pietro Perona},
  journal={2017 IEEE International Conference on Computer Vision (ICCV)},
  year={2017},
  pages={369-378}
}
  • M. R. Ronchi, P. Perona
  • Published 17 July 2017
  • Computer Science
  • 2017 IEEE International Conference on Computer Vision (ICCV)
We propose a new method to analyze the impact of errors in algorithms for multi-instance pose estimation and a principled benchmark that can be used to compare them. We define and characterize three classes of errors - localization, scoring, and background - study how they are influenced by instance attributes and their impact on an algorithm’s performance. Our technique is applied to compare the two leading methods for human pose estimation on the COCO Dataset, measure the sensitivity of pose… 
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