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Recognizing People in Non-Intersecting Camera Views

Recognizing People in Non-Intersecting Camera Views
Recognizing People in Non-Intersecting Camera Views
Many studies have now confirmed that it is possible to recognize people by the way they walk. As yet there has been little formal study of identity tracking using gait over different camera views. We present a new approach for people tracking and identification between different non-intersecting uncalibrated cameras based on gait analysis. An identification signature is derived from gait kinematics as well as anthropometric knowledge. Given the nature of surveillance data, we have developed a new feature extraction technique for finding human legs. The novelty of our approach is motivated by the latest research for people identification using gait. The experimental results confirm the robustness of our method to extract gait features in different scenarios. Furthermore, experimental results revealed the potential of our method to work in real surveillance systems to recognize walking people over different views with achieved cross-camera recognition rates of 95% and 90% for two different views.
Feature extraction, biometrics, camera handoff
Bouchrika, Imed
240fa05b-aed2-400a-a683-b4c0d20f2f68
Carter, John
e05be2f9-991d-4476-bb50-ae91606389da
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Bouchrika, Imed
240fa05b-aed2-400a-a683-b4c0d20f2f68
Carter, John
e05be2f9-991d-4476-bb50-ae91606389da
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12

Bouchrika, Imed, Carter, John and Nixon, Mark (2009) Recognizing People in Non-Intersecting Camera Views. International Conference on Imaging for Crime Detection and Prevention.

Record type: Article

Abstract

Many studies have now confirmed that it is possible to recognize people by the way they walk. As yet there has been little formal study of identity tracking using gait over different camera views. We present a new approach for people tracking and identification between different non-intersecting uncalibrated cameras based on gait analysis. An identification signature is derived from gait kinematics as well as anthropometric knowledge. Given the nature of surveillance data, we have developed a new feature extraction technique for finding human legs. The novelty of our approach is motivated by the latest research for people identification using gait. The experimental results confirm the robustness of our method to extract gait features in different scenarios. Furthermore, experimental results revealed the potential of our method to work in real surveillance systems to recognize walking people over different views with achieved cross-camera recognition rates of 95% and 90% for two different views.

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Published date: 2009
Keywords: Feature extraction, biometrics, camera handoff
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 268178
URI: https://eprints.soton.ac.uk/id/eprint/268178
PURE UUID: d0681264-df92-4a92-a11f-63bb92892ef9
ORCID for Mark Nixon: ORCID iD orcid.org/0000-0002-9174-5934

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Date deposited: 06 Nov 2009 09:50
Last modified: 06 Jun 2018 13:18

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