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Towards automated visual surveillance using gait for identity recognition and tracking across multiple non-intersecting cameras

Towards automated visual surveillance using gait for identity recognition and tracking across multiple non-intersecting cameras
Towards automated visual surveillance using gait for identity recognition and tracking across multiple non-intersecting cameras
Despite the fact that personal privacy has become a major concern, surveillance technology is now becoming ubiquitous in modern society. This is mainly due to the increasing number of crimes as well as the essential necessity to provide secure and safer environment. Recent research studies have confirmed now the possibility of recognizing people by the way they walk i.e. gait. The aim of this research study is to investigate the use of gait for people detection as well as identification across different cameras. We present a new approach for people tracking and identification between different non-intersecting un-calibrated stationary cameras based on gait analysis. A vision-based markerless extraction method is being deployed for the derivation of gait kinematics as well as anthropometric measurements in order to produce a gait signature. The novelty of our approach is motivated by the recent research in biometrics and forensic analysis using gait. The experimental results affirmed the robustness of our approach to successfully detect walking people as well as its potency to extract gait features for different camera viewpoints achieving an identity recognition rate of 73.6 % processed for 2270 video sequences. Furthermore, experimental results confirmed the potential of the proposed method for identity tracking in real surveillance systems to recognize walking individuals across different views with an average recognition rate of 92.5 % for cross-camera matching for two different non-overlapping views.
gait analysis, gait biometrics, markerless extraction
1201-1221
Bouchrika, Imed
584a502f-829f-4acc-9200-e42f60e42bf0
Carter, John N.
e05be2f9-991d-4476-bb50-ae91606389da
Nixon, Mark S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Bouchrika, Imed
584a502f-829f-4acc-9200-e42f60e42bf0
Carter, John N.
e05be2f9-991d-4476-bb50-ae91606389da
Nixon, Mark S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12

Bouchrika, Imed, Carter, John N. and Nixon, Mark S. (2016) Towards automated visual surveillance using gait for identity recognition and tracking across multiple non-intersecting cameras. Multimedia Tools and Applications, 75 (2), 1201-1221.

Record type: Article

Abstract

Despite the fact that personal privacy has become a major concern, surveillance technology is now becoming ubiquitous in modern society. This is mainly due to the increasing number of crimes as well as the essential necessity to provide secure and safer environment. Recent research studies have confirmed now the possibility of recognizing people by the way they walk i.e. gait. The aim of this research study is to investigate the use of gait for people detection as well as identification across different cameras. We present a new approach for people tracking and identification between different non-intersecting un-calibrated stationary cameras based on gait analysis. A vision-based markerless extraction method is being deployed for the derivation of gait kinematics as well as anthropometric measurements in order to produce a gait signature. The novelty of our approach is motivated by the recent research in biometrics and forensic analysis using gait. The experimental results affirmed the robustness of our approach to successfully detect walking people as well as its potency to extract gait features for different camera viewpoints achieving an identity recognition rate of 73.6 % processed for 2270 video sequences. Furthermore, experimental results confirmed the potential of the proposed method for identity tracking in real surveillance systems to recognize walking individuals across different views with an average recognition rate of 92.5 % for cross-camera matching for two different non-overlapping views.

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More information

Accepted/In Press date: 7 November 2014
e-pub ahead of print date: 21 November 2014
Published date: January 2016
Keywords: gait analysis, gait biometrics, markerless extraction
Organisations: Vision, Learning and Control

Identifiers

Local EPrints ID: 372802
URI: https://eprints.soton.ac.uk/id/eprint/372802
PURE UUID: 2751442a-c0a5-4c75-8935-dae62dca923f
ORCID for Mark S. Nixon: ORCID iD orcid.org/0000-0002-9174-5934

Catalogue record

Date deposited: 17 Dec 2014 15:58
Last modified: 06 Jun 2018 13:18

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Contributors

Author: Imed Bouchrika
Author: John N. Carter
Author: Mark S. Nixon ORCID iD

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