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 calibr...
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| Published in | Multimedia tools and applications Vol. 50; no. 1; pp. 75 - 94 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Boston
Springer US
01.10.2010
Springer Nature B.V |
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| Online Access | Get full text |
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| 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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| AbstractList | Issue Title: Special Issue on Analysis and Retrieval of Events/Actions and Workflows in Video Streams; Guest Editors: Anastasios D. Doulamis, Luc van Gool, Mark Nixon, Nikolaos D. Doulamis and Theodora A. Varvarigou 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.[PUBLICATION 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. |
| Author | Nixon, Mark S. Goffredo, Michela Carter, John N. Bouchrika, Imed |
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| Cites_doi | 10.1002/aja.1001200104 10.1109/JPROC.2006.886018 10.1109/AVSS.2003.1217914 10.1109/CVPR.2004.1315165 10.3758/BF03337021 10.1007/978-3-540-76386-4_80 10.1016/S0966-6362(00)00094-1 10.1016/S0954-1810(99)00008-4 10.1109/AFGR.2002.1004181 10.1109/TPAMI.2003.1233912 10.1117/12.424949 10.1109/ICCV.2003.1238451 10.2106/00004623-196446020-00009 10.1007/3-540-47979-1_10 10.1007/11744047_10 10.1109/TPAMI.2003.1251144 10.1109/34.879790 10.1007/3-540-44887-X_71 10.1109/ICCV.2005.122 10.1109/IROS.2006.282619 10.1109/AFGR.2008.4813366 10.5244/C.18.42 10.1109/CCST.1999.797952 10.1109/TSMCC.2004.829274 10.1016/j.imavis.2005.10.007 10.1109/MOTION.2002.1182230 10.1109/ICCV.2005.88 |
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| Keywords | Biometrics Multi-view Surveillance Gait analysis Gait recognition Object handover |
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| References_xml | – reference: BenAbdelkader C, Davis LS, Cutler R (2002) Motion-based recognition of people in eigengait space. In: Proc IEEE conf AFG, pp 267–274. http://csdl.computer.org/comp/proceedings/fgr/2002/1602/00/16020267abs.htm – reference: Middleton L, Wagg DK, Bazin AI, Carter JN, Nixon MS (2006) Developing a non-intrusive biometric environment. In: Proc IEEE int conf IROS, pp 723–728. doi:10.1109/IROS.2006.282619 – reference: Shutler J, Grant M, Nixon MS, Carter JN (2002) On a large sequence-based human gait database. In: Proc int conf recent advances in soft computing, pp 66–72 – reference: WangLTanTHuWNingHAutomatic gait recognition based on statistical shape analysisIEEE Trans IP2003129112011312006856 – reference: Bhanu B, Han J (2003) Human recognition on combining kinematic and stationary features. In: Proc int conf AVBPA, pp 600–608 – reference: Bouchrika I, Nixon MS (2007) Gait-based pedestrian detection for automated surveillance. 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| SubjectTerms | Analysis Automation Biometrics Calibration Cameras Computer Communication Networks Computer Science Cooperation Data compression Data Structures and Information Theory Digital cameras Gait Image processing systems Multimedia Multimedia computer applications Multimedia Information Systems Recognition Special Purpose and Application-Based Systems Studies Surveillance Walking |
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| Title | Performance analysis for automated gait extraction and recognition in multi-camera surveillance |
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