Gait recognition using static, activity-specific parameters

By Aaron F. Bobick, A. Johnson. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, 2001

389 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biomedical Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/05ecbf2cd477ce1e1b12b2c1e78e4fc8dfdc08d2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Biomedical Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

General Tensor Discriminant Analysis and Gabor Features for Gait Recognition (2007, background): “…successful representation models such as appearance-based models [6], [16], [19], [22], [29], [30], [34], [37], [40], stochastic statistical models [19], articulated biomechanics models [36], [3], [1], [7] in which a set of parameters describes the gait, and other parameter-based models [24], [8].”
Silhouette Analysis-Based Gait Recognition for Human Identification (2003, methodology): “These features may be pace, body height, build, and stride length, some of which have been independently used for personal identification in previous algorithms [8], [14], [20].”
Ieee Transactions on Pattern Analysis and Machine Intelligence 1 Matching Shape Sequences in Video with Applications in Human Movement Analysis. Ieee Transactions on Pattern Analysis and Machine Intelligence 2 (neutral): “Bobick and Johnson [36] use activity specific static and stride parameters to perform recognition.”
Silhouette-based human identification from body shape and gait (2002, neutral): “However, the vision research community has only recently begun to investigate gait as a biometric [1, 7, 8, 9, 11, 13, 14].”
Gait Components and Their Application to Gender Recognition (2008, background): “Many other efforts have also been spent on gait information processing [2], [4], [6], [9], [10], [12], [19], [22], [38], and since then, gait silhouette information has been widely utilized, e.”
The humanID gait challenge problem: data sets, performance, and analysis (2005, background): “It is only recently that human identification (HumanID ) from gait has received attention and become an active area of computer vision [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29].”
Gait Recognition Using a View Transformation Model in the Frequency Domain (2006, neutral): “[3] extracted parameters of shape and stride.”
Biometric Gait Authentication Using Accelerometer Sensor (2006, background): “Some of them alternatively report their results in terms of DET curves [36], [37], [38].”
Fusion of static and dynamic body biometrics for gait recognition (2003, positive): “Interest in automatic gait recognition in the computer vision community only began recently, but considerable efforts have already been made [2], [4], [11], [12], [14]–[16], [23], [26]–[28].”
Identification of humans using gait (2004, background): “In [6] activity specific static parameters are extracted for gait recognition.”
What Else Does Your Biometric Data Reveal? A Survey on Soft Biometrics (2016, background): “In a similar spirit, but in the setting of person identification, Johnson and Bobick [229] extracted four static parameters (body height, torso length, leg length, and step length), achieving a typical error of about 30cm for anthropometric measure estimation.”
Automatic gait recognition based on statistical shape analysis (2003, methodology): “[20] A. Johnson and A. Bobick, “A multi-view method for gait recognition using static body parameters,” inProc.”
Social signal processing: Survey of an emerging domain (2009, background): “distance between head and feets or pelvis) [17], etc.”