Visual Surveillance and Tracking of Humans by Face and Gait Recognition
Visual Surveillance and Tracking of Humans by Face and Gait Recognition
Increased emphasis on automated real time intelligent surveillance system has led to the need to identify and track people in complex environments. Independent features such as face, gait provide valuable clues as to identity, which coupled with data fusion and tracking algorithms offer a potential solution to this problem. In this paper we address the first problem of recognizing humans in real time, data fusion and tracking will be performed by neurofuzzy state estimators. A new approach which combines eigenspace transformation with canonical space transformation is proposed here. This method can be used to reduce data dimensionality and to optimize the class separability of different classes simultaneously.
43-44
Huang, P.S.
a46d0155-1e6b-4874-ae22-b199c22d2f28
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Nixon, M.S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
October 1998
Huang, P.S.
a46d0155-1e6b-4874-ae22-b199c22d2f28
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Nixon, M.S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Huang, P.S., Harris, C.J. and Nixon, M.S.
(1998)
Visual Surveillance and Tracking of Humans by Face and Gait Recognition.
Proc. of 7th IFAC Symposium on Artificial Intelligence in Real-Time Control.
.
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Conference or Workshop Item
(Other)
Abstract
Increased emphasis on automated real time intelligent surveillance system has led to the need to identify and track people in complex environments. Independent features such as face, gait provide valuable clues as to identity, which coupled with data fusion and tracking algorithms offer a potential solution to this problem. In this paper we address the first problem of recognizing humans in real time, data fusion and tracking will be performed by neurofuzzy state estimators. A new approach which combines eigenspace transformation with canonical space transformation is proposed here. This method can be used to reduce data dimensionality and to optimize the class separability of different classes simultaneously.
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Published date: October 1998
Additional Information:
Extended Version on CD Organisation: IFAC Address: Grand Canyon National Park, Arizona, USA
Venue - Dates:
Proc. of 7th IFAC Symposium on Artificial Intelligence in Real-Time Control, 1998-09-30
Organisations:
Southampton Wireless Group
Identifiers
Local EPrints ID: 250438
URI: http://eprints.soton.ac.uk/id/eprint/250438
PURE UUID: 1a9dd03f-4a4a-465b-bb63-7c3a93a3f941
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Date deposited: 01 Dec 1999
Last modified: 11 Dec 2021 02:38
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Contributors
Author:
P.S. Huang
Author:
C.J. Harris
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