Temporal PDMs for gait classification

Tassone, E. P.; West, Geoff; Venkatesh, Svetha

doi:10.1109/icpr.2002.1048489

Cited by 13 publications

(8 citation statements)

References 10 publications

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“…From the two tables, we get the information of recognition rate, different with 4 W [17], which can achieve 86%. The reasons may be resulted from: 1) From table 1, the proposed method has similar correct recognition rate with 4 W , because these four movements are very different from silhouette contour.…”

Section: ． Evaluation and Experimental Resultsmentioning

confidence: 98%

A Research of Motion Classification in Gait Recognition

Xue

Liu

2009

2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery

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Motion classification is the first step of gait recognition. The classification of motion is conducted, and behavior validity can be made under specific scenarios. In order to identify people movement in an Intelligent Security Monitoring System, moving body is detected and the boundary is extracted. The paper proposes a complex number notation based on centroid in order to indicate a pedestrian's movements. And according to the different sorts of movements, a set of standard image contours are made. A Procrustes shape analysis method is presented in order to get the degree to which two contours are resembled. Finally lying, bending, sitting, walking, side walking, jumping, crouching, uphill and downhill is given with a 72.2% recognition rate achieved.

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Section: ． Evaluation and Experimental Resultsmentioning

confidence: 98%

A Research of Motion Classification in Gait Recognition

Xue

Liu

2009

2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery

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“…This pattern is reflected in the current approaches, all but one are based on analysis of silhouettes, including: the University of Maryland's (UM's) deployment of hidden Markov models [14] and eigenanalysis [15]; the National Institute for Standards in Technology/University of South Florida's (NIST/USF's) baseline approach matching silhouettes [16]; Georgia Technical Research Institute's (GTRI's) data derivation of stride pattern [17]; Carnegie Mellon University's (CMU's) use of key frame analysis for sequence matching [18]; Massachusetts Institute of Technology's (MIT's) ellipsoidal fits [19]; Curtin's use of Point Distribution Models [20] and the Chinese Academy of Science's eigenspace transformation of an unwrapped human silhouette [21]. These show promise for baseline approaches that impose low computational and storage cost, together with deployment and development of new computer vision techniques for sequence-based analysis.…”

Section: Automatic Recognition By Gaitmentioning

confidence: 99%

New Advances in Automatic Gait Recognition

Nixon

Carter

Shutler

et al. 2002

Information Security Technical Report

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Recognising people by their gait is an emergent biometric. Until recently there was evaluation by few techniques on relatively small databases though with encouraging results. The potential of gait as a biometric has further been encouraged by the considerable amount of evidence available, especially in medicine and literature. This evident potential motivated development of new databases, new technique and more rigorous evaluation procedures. We describe the new techniques we have developed and their evaluation on our database to gain insight into the potential for gait as a biometric. We also describe some of our new approaches aimed to aid generalization capability for deployment of gait recognition. We show on these new and much larger databases, how our novel techniques continue to provide encouraging results for gait as a biometric, let alone as a human identifier, with especial regard for recognition at a distance.

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“…Massachusetts Institute of Technology's (MIT's) ellipsoidal fits [18]; Curtin's use of Point Distribution Models [19] and the Chinese Academy of Science's eigenspace transformation of an unwrapped human silhouette [20]. Only the latter two Institutions were not involved in the HiD program.…”

Section: Recognising People By Their Gaitmentioning

confidence: 99%

Automatic Gait Recognition for Human ID at a Distance

Nixon¹,

Carter²

2004

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Report Documentation PageForm Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Summary:Recognising people by their gait is a biometric of increasing interest. Now, analysis has progressed from evaluation by few techniques on small databases with encouraging results to large databases and still with encouraging results.The potential of gait as a biometric was encouraged by the considerable amount of evidence available, especially in biomechanics and literature.This report describes research within the Human ID ( The research reported herein has been sponsored in part by the United States Army through its EuropeanResearch Office

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Temporal PDMs for gait classification

Cited by 13 publications

References 10 publications

A Research of Motion Classification in Gait Recognition

A Research of Motion Classification in Gait Recognition

New Advances in Automatic Gait Recognition

Automatic Gait Recognition for Human ID at a Distance

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