Improved Gait Recognition using Gradient Histogram Energy Image

Martin Hofmann, Gerhard Rigoll

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

28 Zitate (Scopus)

Abstract

We present a new spatio-temporal representation for Gait Recognition, which we call Gradient Histogram Energy Image (GHEI). Similar to the successful Gait Energy Image (GEI), information is averaged over full gait cycles to reduce noise. Contrary to GEI, where silhouettes are averaged and thus only edge information at the boundary is used, our GHEI computes gradient histograms at all locations of the original image. Thus, also edge information inside the person silhouette is captured. In addition, we show that GHEI can be greatly improved using precise segmentation techniques (we use a-matte segmentation). We demonstrate great effectiveness of GHEI and its variants in our experiments on the large and widely used HumanID Gait Challenge dataset. On this dataset we reach a significant performance gain over the current state of the art.

OriginalspracheEnglisch
Titel2012 IEEE International Conference on Image Processing, ICIP 2012 - Proceedings
Seiten1389-1392
Seitenumfang4
DOIs
PublikationsstatusVeröffentlicht - 2012
Veranstaltung2012 19th IEEE International Conference on Image Processing, ICIP 2012 - Lake Buena Vista, FL, USA/Vereinigte Staaten
Dauer: 30 Sept. 20123 Okt. 2012

Publikationsreihe

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Konferenz

Konferenz2012 19th IEEE International Conference on Image Processing, ICIP 2012
Land/GebietUSA/Vereinigte Staaten
OrtLake Buena Vista, FL
Zeitraum30/09/123/10/12

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