Gesture recognition performance score

A new metric to evaluate gesture recognition systems

Pramod Kumar Pisharady, Martin Saerbeck

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

1 Citation (Scopus)

Abstract

In spite of many choices available for gesture recognition algorithms, the selection of a proper algorithm for a specific application remains a difficult task. The available algorithms have different strengths and weaknesses making the matching between algorithms and applications complex. Accurate evaluation of the performance of a gesture recognition algorithm is a cumbersome task. Performance evaluation by recognition accuracy alone is not sufficient to predict its successful realworld implementation. We developed a novel Gesture Recognition Performance Score (GRPS) for ranking gesture recognition algorithms, and to predict the success of these algorithms in real-world scenarios. The GRPS is calculated by considering different attributes of the algorithm, the evaluation methodology adopted, and the quality of dataset used for testing. The GRPS calculation is illustrated and applied on a set of vision based hand/ arm gesture recognition algorithms reported in the last 15 years. Based on GRPS a ranking of hand gesture recognition algorithms is provided. The paper also presents an evaluation metric namely Gesture Dataset Score (GDS) to quantify the quality of gesture databases. The GRPS calculator and results are made publicly available (http://software.ihpc.a-star.edu.sg/grps/).

Original languageEnglish (US)
Title of host publicationComputer Vision - ACCV 2014 Workshops - Revised Selected Papers
PublisherSpringer Verlag
Pages157-173
Number of pages17
Volume9008
ISBN (Print)9783319166278
DOIs
StatePublished - Jan 1 2015
Event12th Asian Conference on Computer Vision, ACCV 2014 - Singapore, Singapore
Duration: Nov 1 2014Nov 2 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9008
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other12th Asian Conference on Computer Vision, ACCV 2014
CountrySingapore
CitySingapore
Period11/1/1411/2/14

Fingerprint

Gesture recognition
Gesture Recognition
Recognition Algorithm
Metric
Evaluate
Hand Gesture Recognition
Gesture
Ranking
Evaluation
Predict
Calculator
Performance Evaluation
Star
Quantify
Attribute
Stars
Sufficient
Scenarios
Testing
Software

Cite this

Pisharady, P. K., & Saerbeck, M. (2015). Gesture recognition performance score: A new metric to evaluate gesture recognition systems. In Computer Vision - ACCV 2014 Workshops - Revised Selected Papers (Vol. 9008, pp. 157-173). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9008). Springer Verlag. https://doi.org/10.1007/978-3-319-16628-5_12

Gesture recognition performance score : A new metric to evaluate gesture recognition systems. / Pisharady, Pramod Kumar; Saerbeck, Martin.

Computer Vision - ACCV 2014 Workshops - Revised Selected Papers. Vol. 9008 Springer Verlag, 2015. p. 157-173 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9008).

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

Pisharady, PK & Saerbeck, M 2015, Gesture recognition performance score: A new metric to evaluate gesture recognition systems. in Computer Vision - ACCV 2014 Workshops - Revised Selected Papers. vol. 9008, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9008, Springer Verlag, pp. 157-173, 12th Asian Conference on Computer Vision, ACCV 2014, Singapore, Singapore, 11/1/14. https://doi.org/10.1007/978-3-319-16628-5_12
Pisharady PK, Saerbeck M. Gesture recognition performance score: A new metric to evaluate gesture recognition systems. In Computer Vision - ACCV 2014 Workshops - Revised Selected Papers. Vol. 9008. Springer Verlag. 2015. p. 157-173. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-16628-5_12
Pisharady, Pramod Kumar ; Saerbeck, Martin. / Gesture recognition performance score : A new metric to evaluate gesture recognition systems. Computer Vision - ACCV 2014 Workshops - Revised Selected Papers. Vol. 9008 Springer Verlag, 2015. pp. 157-173 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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