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Benchmarking and Error Diagnosis in Multi-instance Pose Estimation

Ronchi, Matteo Ruggero and Perona, Pietro (2017) Benchmarking and Error Diagnosis in Multi-instance Pose Estimation. In: 2017 IEEE International Conference on Computer Vision (ICCV). IEEE , Piscataway, NJ, pp. 369-378. ISBN 978-1-5386-1032-9.

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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 estimation with respect to instance size, type and number of visible keypoints, clutter due to multiple instances, and the relative score of instances. The performance of algorithms, and the types of error they make, are highly dependent on all these variables, but mostly on the number of keypoints and the clutter. The analysis and software tools we propose offer a novel and insightful approach for understanding the behavior of pose estimation algorithms and an effective method for measuring their strengths and weaknesses.

Item Type:Book Section
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URLURL TypeDescription Paper
Perona, Pietro0000-0002-7583-5809
Additional Information:© 2017 IEEE.
Record Number:CaltechAUTHORS:20180314-141340921
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Official Citation:M. R. Ronchi and P. Perona, "Benchmarking and Error Diagnosis in Multi-instance Pose Estimation," 2017 IEEE International Conference on Computer Vision (ICCV), Venice, 2017, pp. 369-378. doi: 10.1109/ICCV.2017.48 URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:85312
Deposited By: Tony Diaz
Deposited On:15 Mar 2018 03:34
Last Modified:15 Nov 2021 20:27

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