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Performance Analysis for Automated Gait Extraction and Recognition in Multi-Camera Surveillance

Performance Analysis for Automated Gait Extraction and Recognition in Multi-Camera Surveillance
Performance Analysis for Automated Gait Extraction and Recognition in Multi-Camera Surveillance
Many studies have confirmed that gait analysis can be used as a new biometrics. In this research, gait analysis is deployed for people identification in multi-camera surveillance scenarios. We present a new method for viewpoint independent markerless gait analysis that does not require camera calibration and works with a wide range of walking directions. These properties make the proposed method particularly suitable for gait identification in real surveillance scenarios where people and their behaviour need to be tracked across a set of cameras. Tests on 300 synthetic and real video sequences, with subjects walking freely along different walking directions, have been performed. Since the choice of the cameras' characteristics is a key-point for the development of a smart surveillance system, the performance of the proposed approach is measured with respect to different video properties: spatial resolution, frame-rate, data compression and image quality. The obtained results show that markerless gait analysis can be achieved without any knowledge of camera's position and subject's pose. The extracted gait parameters allow recognition of people walking from different views with a mean recognition rate of 92.2% and confirm that gait can be effectively used for subjects' identification in a multi-camera surveillance scenario.
Goffredo, Michela
21a346d2-8ce6-46b7-883f-89a2c584afc7
Bouchrika, Imed
240fa05b-aed2-400a-a683-b4c0d20f2f68
Carter, John
e05be2f9-991d-4476-bb50-ae91606389da
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Goffredo, Michela
21a346d2-8ce6-46b7-883f-89a2c584afc7
Bouchrika, Imed
240fa05b-aed2-400a-a683-b4c0d20f2f68
Carter, John
e05be2f9-991d-4476-bb50-ae91606389da
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12

Goffredo, Michela, Bouchrika, Imed, Carter, John and Nixon, Mark (2009) Performance Analysis for Automated Gait Extraction and Recognition in Multi-Camera Surveillance. Journal of Multimedia Tools and Application.

Record type: Article

Abstract

Many studies have confirmed that gait analysis can be used as a new biometrics. In this research, gait analysis is deployed for people identification in multi-camera surveillance scenarios. We present a new method for viewpoint independent markerless gait analysis that does not require camera calibration and works with a wide range of walking directions. These properties make the proposed method particularly suitable for gait identification in real surveillance scenarios where people and their behaviour need to be tracked across a set of cameras. Tests on 300 synthetic and real video sequences, with subjects walking freely along different walking directions, have been performed. Since the choice of the cameras' characteristics is a key-point for the development of a smart surveillance system, the performance of the proposed approach is measured with respect to different video properties: spatial resolution, frame-rate, data compression and image quality. The obtained results show that markerless gait analysis can be achieved without any knowledge of camera's position and subject's pose. The extracted gait parameters allow recognition of people walking from different views with a mean recognition rate of 92.2% and confirm that gait can be effectively used for subjects' identification in a multi-camera surveillance scenario.

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Published date: 2009
Organisations: Southampton Wireless Group

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Local EPrints ID: 268179
URI: https://eprints.soton.ac.uk/id/eprint/268179
PURE UUID: 1bc0cd6a-9e05-478f-bb03-febe07e34572
ORCID for Mark Nixon: ORCID iD orcid.org/0000-0002-9174-5934

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Date deposited: 06 Nov 2009 09:59
Last modified: 06 Jun 2018 13:18

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Contributors

Author: Michela Goffredo
Author: Imed Bouchrika
Author: John Carter
Author: Mark Nixon ORCID iD

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