To cite this record: http://ddd.uab.cat/record/52588
Gait Identification Considering Body Tilt byWalking Direction Changes
Makihara, Yasushi
Sagawa, Ryusuke
Mukaigawa, Yasuhiro
Echigo, Tomio
Yagi, Yasushi

Date: 2009
Abstract: Gait identification has recently gained attention as a method of identifying individuals at a distance. Thought most of the previous works mainly treated straight-walk sequences for simplicity, curved-walk sequences should be also treated considering situations where a person walks along a curved path or enters a building from a sidewalk. In such cases, person’s body sometimes tilts by centrifugal force when walking directions change, and this body tilt considerably degrades gait silhouette and identification performance, especially for widely-used appearance-based approaches. Therefore, we propose a method of body-tilted silhouette correction based on centrifugal force estimation from walking trajectories. Then, gait identification process including gait feature extraction in the frequency domain and learning of a View Transformation Model (VTM) follows the silhouette correction. Experiments of gait identification for circular-walk sequences demonstrate the effectiveness of the proposed method.
Rights: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades. Creative Commons
Language: Anglès.
Document: article ; recerca ; publishedVersion
Subject: Identificació de la marxa ; Correcció de l'inclinació ; L'anàlisi de Fourier ; Veure model de transformació ; Identificación de la marcha ; Corrección de inclinación ; Análisis Fourier ; Ver modelo de transformación ; Gait identification ; Body tilt correction ; Fourier analysis ; View Transformation Model
Published in: ELCVIA : Electronic Letters on Computer Vision and Image Analysis, V. 8 n. 1 (2009) p. 15-26, ISSN 1577-5097



12 p, 266.4 KB

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Articles > Published articles > ELCVIA : Electronic Letters on Computer Vision and Image Analysis

 Record created 2010-01-18, last modified 2014-10-29



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