Invariant Classification of Gait Types

Preben Fihl, Thomas B. Moeslund

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Resumé

This paper presents a method of classifying human gait in an invariant manner based on silhouette comparison. A database of artificially generated silhouettes is created representing the three main types of gait, i.e. walking, jogging, and running. Silhouettes generated from different camera angles are included in the database to make the method invariant to camera viewpoint and to changing directions of movement. The extraction of silhouettes are done using the Codebook method and silhouettes are represented in a scale- and translation-invariant manner by using shape contexts and tangent orientations. Input silhouettes are matched to the database using the Hungarian method. A classifier is defined based on the dissimilarity between the input silhouettes and the gait actions of the database. The overall recognition rate is 88.2% on a large and diverse test set. The recognition rate is better than that achieved by other approaches applied to similar data.

OriginalsprogEngelsk
TitelCanadian Conference on Computer and Robot Vision, 2008. CRV '08
Antal sider7
ForlagIEEE
Publikationsdato2008
Sider179-185
ISBN (Trykt)978-0-7695-3153-3
DOI
StatusUdgivet - 2008
BegivenhedCanadian Conference on Computer and Robot Vision - Windsor, Ontario, Canada
Varighed: 28 maj 200830 maj 2008
Konferencens nummer: 5th

Konference

KonferenceCanadian Conference on Computer and Robot Vision
Nummer5th
LandCanada
ByWindsor, Ontario
Periode28/05/200830/05/2008

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Fihl, P., & Moeslund, T. B. (2008). Invariant Classification of Gait Types. I Canadian Conference on Computer and Robot Vision, 2008. CRV '08 (s. 179-185). IEEE. https://doi.org/10.1109/CRV.2008.24
Fihl, Preben ; Moeslund, Thomas B. / Invariant Classification of Gait Types. Canadian Conference on Computer and Robot Vision, 2008. CRV '08. IEEE, 2008. s. 179-185
@inproceedings{fae26130993511deb96d000ea68e967b,
title = "Invariant Classification of Gait Types",
abstract = "This paper presents a method of classifying human gait in an invariant manner based on silhouette comparison. A database of artificially generated silhouettes is created representing the three main types of gait, i.e. walking, jogging, and running. Silhouettes generated from different camera angles are included in the database to make the method invariant to camera viewpoint and to changing directions of movement. The extraction of silhouettes are done using the Codebook method and silhouettes are represented in a scale- and translation-invariant manner by using shape contexts and tangent orientations. Input silhouettes are matched to the database using the Hungarian method. A classifier is defined based on the dissimilarity between the input silhouettes and the gait actions of the database. The overall recognition rate is 88.2{\%} on a large and diverse test set. The recognition rate is better than that achieved by other approaches applied to similar data.",
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Fihl, P & Moeslund, TB 2008, Invariant Classification of Gait Types. i Canadian Conference on Computer and Robot Vision, 2008. CRV '08. IEEE, s. 179-185, Canadian Conference on Computer and Robot Vision, Windsor, Ontario, Canada, 28/05/2008. https://doi.org/10.1109/CRV.2008.24

Invariant Classification of Gait Types. / Fihl, Preben; Moeslund, Thomas B.

Canadian Conference on Computer and Robot Vision, 2008. CRV '08. IEEE, 2008. s. 179-185.

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

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AB - This paper presents a method of classifying human gait in an invariant manner based on silhouette comparison. A database of artificially generated silhouettes is created representing the three main types of gait, i.e. walking, jogging, and running. Silhouettes generated from different camera angles are included in the database to make the method invariant to camera viewpoint and to changing directions of movement. The extraction of silhouettes are done using the Codebook method and silhouettes are represented in a scale- and translation-invariant manner by using shape contexts and tangent orientations. Input silhouettes are matched to the database using the Hungarian method. A classifier is defined based on the dissimilarity between the input silhouettes and the gait actions of the database. The overall recognition rate is 88.2% on a large and diverse test set. The recognition rate is better than that achieved by other approaches applied to similar data.

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Fihl P, Moeslund TB. Invariant Classification of Gait Types. I Canadian Conference on Computer and Robot Vision, 2008. CRV '08. IEEE. 2008. s. 179-185 https://doi.org/10.1109/CRV.2008.24