Has My Algorithm Succeeded? An Evaluator for Human Pose Estimators

Nataraj Jammalamadaka, Andrew Zisserman, M. Eichner, Vittorio Ferrari, C. V. Jawahar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Most current vision algorithms deliver their output ‘as is’, without indicating whether it is correct or not. In this paper we propose evaluator algorithms that predict if a vision algorithm has succeeded. We illustrate this idea for the case of Human Pose Estimation (HPE).

We describe the stages required to learn and test an evaluator, including the use of an annotated ground truth dataset for training and testing the evaluator (and we provide a new dataset for the HPE case), and the development of auxiliary features that have not been used by the (HPE) algorithm, but can be learnt by the evaluator to predict if the output is correct or not.

Then an evaluator is built for each of four recently developed HPE algorithms using their publicly available implementations: Eichner and Ferrari [5], Sapp et al. [16], Andriluka et al. [2] and Yang and Ramanan [22]. We demonstrate that in each case the evaluator is able to predict if the algorithm has correctly estimated the pose or not.
Original languageEnglish
Title of host publicationComputer Vision – ECCV 2012
Subtitle of host publication12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part III
PublisherSpringer Berlin Heidelberg
Number of pages15
ISBN (Electronic)978-3-642-33712-3
ISBN (Print)978-3-642-33711-6
Publication statusPublished - 2012

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin Heidelberg
ISSN (Print)0302-9743


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