Human gait recognition in canonical space using temporal templates

Huang, Ping; Harris, C.J.; Nixon, Mark S.

doi:10.1049/ip-vis:19990187

Cited by 132 publications

(91 citation statements)

References 17 publications

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“…This choice is justified by the fact that the lateral view has proven recognition capability with many approaches [6] and that the pose-free approach works on a small number of camera positions.…”

Section: Gait-based Identification For Trackingmentioning

confidence: 99%

Recognizing people in non-intersecting camera views

Bouchrika

Carter

Nixon

2009

3rd International Conference on Imaging for Crime Detection and Prevention (ICDP 2009)

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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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Section: Gait-based Identification For Trackingmentioning

confidence: 99%

Recognizing people in non-intersecting camera views

Bouchrika

Carter

Nixon

2009

3rd International Conference on Imaging for Crime Detection and Prevention (ICDP 2009)

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“…Further, the approach appears robust to noise in the input images. This was later extended to include Canonical Analysis (CA) with better discriminatory capability [20], and extended to analyse flow rather than just silhouettes -to better effect [21].…”

Section: Early Approachesmentioning

confidence: 99%

Advances in automatic gait recognition

Nixon

Carter

Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings.

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“…Although gait recognition is a very new research area, there have been some attempts in the recent literature [2][3][4][5][6][7][8][9][10][11][12][13][14]. They can be simply divided into two main classes [10].…”

Section: Introductionmentioning

confidence: 99%

“…They can be simply divided into two main classes [10]. The first class, state-space methods, considers gait to be comprised of a sequence of body poses, and recognizes it through considering temporal variations of observations with respect to those static poses [2,3]. For instance, Murase and Sakai [2] presented a template matching method using eigenspace representation to distinguish different gaits.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, Murase and Sakai [2] presented a template matching method using eigenspace representation to distinguish different gaits. Huang et al [3] extended the approach of [2] by adding canonical analysis. The second class, spatio-temporal methods, characterizes the spatio-temporal distribution generated by the motion in its continuum [5][6][7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

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Gait recognition based on Procrustes shape analysis

Wang

Ning

et al.

Proceedings. International Conference on Image Processing

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Gait recognition has recently attracted increasing attention, especially in vision-based human identification at a distance in visual surveillance. This paper proposes a simple but efficient gait recognition algorithm based on statistical shape analysis. For each gait sequence, a background subtraction procedure is used to segment spatial silhouettes of the walking figures from the background. Static pose changes of these silhouettes over time are represented as a sequence of associated complex configurations in a common coordinate, and are then analyzed using the Procrustes shape analysis method to obtain gait signature. The k-nearest neighbor classifier and the nearest exemplar classifier based on the full Procrustes distance measure are adopted for recognition. Experimental results demonstrate that the proposed algorithm has an encouraging recognition performance.

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Human gait recognition in canonical space using temporal templates

Cited by 132 publications

References 17 publications

Recognizing people in non-intersecting camera views

Recognizing people in non-intersecting camera views

Advances in automatic gait recognition

Gait recognition based on Procrustes shape analysis

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