Hexagonal Volume Local Binary Pattern (H-VLBP) with deep stacked autoencoder for Human Action Recognition

Human action recognition plays a significant role in a number of computer vision applications. This work is based on three processing stages. In the first stage, discriminative frames are selected as representative frames per action to minimize the computational cost and time. In the second stage, n...

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Vydané v:Cognitive systems research Ročník 58; s. 71 - 93
Hlavní autori: Kiruba, K, Shiloah, Elizabeth D, Sunil, Retmin Raj C
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 01.12.2019
Predmet:
Action recognition
Deep stacked autoencoder
H-VLBP
Local binary pattern
Action recognition
Local binary pattern
H-VLBP
Deep stacked autoencoder
ISSN:1389-0417, 1389-0417
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Shrnutí:Human action recognition plays a significant role in a number of computer vision applications. This work is based on three processing stages. In the first stage, discriminative frames are selected as representative frames per action to minimize the computational cost and time. In the second stage, novel neighbourhood selection approaches based on geometric shapes including triangle, quadrilateral, pentagon, hexagon, octagon and heptagon are used in Volumetric Local Binary Pattern (VLBP) to extract the features from frame sequences based on motion and appearance information. Hexagonal Volume Local Binary Pattern (H-VLBP) descriptor has been found to produce better results among all other novel geometric shape based neighbourhood selection approaches for human action recognition. However, the dimensionality of extracted feature from H-VLBP is too large. Therefore, the deep stacked autoencoder is used for dimensionality reduction with the decoder layer replaced by softmax layer for performing multi-class recognition. The developed approach is applied to four publicly available benchmark datasets, namely KTH, Weizmann, UCF11 dataset and IXMAS dataset for human action recognition. The results obtained show that the proposed approach outperforms the state-of-art techniques. Moreover, the approach has been tested with a synthetic dataset and better results have been obtained. This illustrates the effectiveness of the approach in real time environment.
ISSN:1389-0417
1389-0417
DOI:10.1016/j.cogsys.2019.03.001