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Analysis of Human Gait for Person Identification and Human Action Recognition

Deepak N.A., Sinha U.N.. Published in Pattern Recognition.

Communications on Applied Electronics
Year of Publication: 2016
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Deepak N.A., Sinha U.N.
10.5120/cae2016652080

Deepak N.A. and Sinha U.N.. Article: Analysis of Human Gait for Person Identification and Human Action Recognition. Communications on Applied Electronics 4(4):1-4, February 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

@article{key:article,
	author = {Deepak N.A. and Sinha U.N.},
	title = {Article: Analysis of Human Gait for Person Identification and Human Action Recognition},
	journal = {Communications on Applied Electronics},
	year = {2016},
	volume = {4},
	number = {4},
	pages = {1-4},
	month = {February},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}
}

Abstract

The gait sequence analysis finds applications in several fields such as, person identification, human action recognition and event classification. The extraction of gait sequences is the initial step in gait sequence analysis. The Latent Dirichlet Allocation (LDA) a type of ’Topic Model’ is used to analyse the gait sequences for person identification and human action recognition. This is achieved by proposing a novel method that transforms the gait sequences into words representation. Finally, the generated words are then analysed using LDA for person identification and human action recognition. The proposed person identification algorithm is tested using CASIA gait dataset A and human action recognition algorithm is tested using Weizmann action recognition dataset, resulting in correct classification rate of 85%using CASIA Dataset A and 85% using Weizmann action recognition dataset.

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Keywords

Binary Silhouettes, Gait Analysis, Images-Topics-Words, Transformation, Latent Dirichlet Allocation