Gait probability image: An information-theoretic model of gait representation

Lee, Chin Poo; Tan, Alan Wee Chiat; Tan, Shing Chiang

doi:10.1016/j.jvcir.2014.05.006

Cited by 33 publications

(20 citation statements)

References 25 publications

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“…In Tab. 7, each row corresponds to a different number of frames in the range [5,40]. The dictionary size has been fixed to either 50 or 100 (column 'K') on the configuration 'PFM+PCALx20+PCAH256-ft0110'.…”

Section: Methodsmentioning

confidence: 99%

Fisher Motion Descriptor for Multiview Gait Recognition

Castro

Marín-Jiménez

Guil

et al. 2017

Int. J. Patt. Recogn. Artif. Intell.

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The goal of this paper is to identify individuals by analyzing their gait. Instead of using binary silhouettes as input data (as done in many previous works) we propose and evaluate the use of motion descriptors based on densely sampled short-term trajectories. We take advantage of state-of-the-art people detectors to define custom spatial configurations of the descriptors around the target person, obtaining a rich representation of the gait motion. The local motion features (described by the Divergence-Curl-Shear descriptor

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Section: Methodsmentioning

confidence: 99%

Fisher Motion Descriptor for Multiview Gait Recognition

Castro

Marín-Jiménez

Guil

et al. 2017

Int. J. Patt. Recogn. Artif. Intell.

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“…They considered the silhouette as a multivariate random variable and Bernoulli mixture model was employed to model silhouette distribution. Lee et al (2014a) propounded yet another probabilistic gait representation by computing the binomial distribution of all pixels in the gait image. Thereafter, the mean and variance of the distribution is obtained.…”

Section: Distribution-based Representationmentioning

confidence: 99%

Review on Vision-Based Gait Recognition: Representations, Classification Schemes and Datasets

Lee¹,

Tan²,

Lim³

2017

American Journal of Applied Sciences

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Gait has unique advantage at a distance when other biometrics cannot be used since they are at too low resolution or obscured, as commonly observed in visual surveillance systems. This paper provides a survey of the technical advancements in vision-based gait recognition. A wide range of publications are discussed in this survey embracing different perspectives of the research in this area, including gait feature extraction, classification schemes and standard gait databases. There are two major groups of the state-of-the-art techniques in characterizing gait: Model-based and motion-free. The model-based approach obtains a set of body or motion parameters via human body or motion modeling. The model-free approach, on the other hand, derives a description of the motion without assuming any model. Each major category is further organized into several subcategories based on the nature of gait representation. In addition, some widely used classification schemes and benchmark databases for evaluating performance are also discussed.

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“…FDEI works well on silhouettes subjected to incompleteness or occlusion. Recently, a probabilistic gait image (GPI) is presented (Lee et al, 2014). In this work, the binomial distribution of all the pixels in a gait cycle is obtained and symmetric Kullback-Leibler divergence is then used for recognition.…”

Section: Related Workmentioning

confidence: 99%