People Detection and Recognition using Gait for Automated Visual Surveillance
People Detection and Recognition using Gait for Automated Visual Surveillance
In this paper, a computer vision system for automated visual surveillance in an unconstrained outdoor environment is described. We propose a method for tracking multiple moving objects based on shape-based feature correspondence between consecutive frames. We have explored a new approach for walking people detection and recognition based on their gait motion. The novelty of our approach is motivated by the latest research for people identification using gait. The gait signature is derived using a model-based method. The experimental results confirmed the robustness of our method to discriminate between single walking people, groups of people and vehicles with a detection rate of %100. Furthermore, the system is able to recognize walking people with a CCR of %92.
BOUCHRIKA, I
b294dfd3-6686-49b1-af2f-0ed0ebc3c5f5
NIXON, M. S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
2006
BOUCHRIKA, I
b294dfd3-6686-49b1-af2f-0ed0ebc3c5f5
NIXON, M. S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
BOUCHRIKA, I and NIXON, M. S.
(2006)
People Detection and Recognition using Gait for Automated Visual Surveillance.
IEE International Symposium on Imaging for Crime Detection and Prevention, London, United Kingdom.
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Abstract
In this paper, a computer vision system for automated visual surveillance in an unconstrained outdoor environment is described. We propose a method for tracking multiple moving objects based on shape-based feature correspondence between consecutive frames. We have explored a new approach for walking people detection and recognition based on their gait motion. The novelty of our approach is motivated by the latest research for people identification using gait. The gait signature is derived using a model-based method. The experimental results confirmed the robustness of our method to discriminate between single walking people, groups of people and vehicles with a detection rate of %100. Furthermore, the system is able to recognize walking people with a CCR of %92.
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Published date: 2006
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Event Dates: June, 2006
Venue - Dates:
IEE International Symposium on Imaging for Crime Detection and Prevention, London, United Kingdom, 2006-06-01
Organisations:
Southampton Wireless Group
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Local EPrints ID: 262814
URI: http://eprints.soton.ac.uk/id/eprint/262814
PURE UUID: 05ea2dfb-ec5e-4cb6-8a4c-60ace9e55821
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Date deposited: 07 Jul 2006
Last modified: 15 Mar 2024 02:35
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Author:
I BOUCHRIKA
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