Human action recognition with 3D local binary patterns and dense micro-block difference description

M. Zhdanova; V. Voronin; E. Semenishchev; Yu. Ilyukhin; A. Zelensky

doi:10.1117/12.2588291

12 April 2021 Human action recognition with 3D local binary patterns and dense micro-block difference description

M. Zhdanova, V. Voronin, E. Semenishchev, Yu. Ilyukhin, A. Zelensky

Proceedings Volume 11729, Automatic Target Recognition XXXI; 117290H (2021) https://doi.org/10.1117/12.2588291
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

This paper describes an action recognition method based on the 3D local binary dense micro-block difference. The proposed algorithm is a three-stage procedure: (a) image preprocessing using a 3D Gabor filter, (b) a descriptor calculation using 3D local binary dense micro-block difference with skeleton points, and (c) SVM classification. The proposed algorithm is based on capturing 3D sub-volumes located inside a video sequence patch and calculating the difference in intensities between these sub-volumes. For intensifies motion used the convolution with a bank of 3D arbitrarily-oriented Gabor filters. We calculate the local features for pre-processed frames, such as 3D local binary dense micro-block difference (3D LBDMD). We evaluate the proposed approach on the UCF101 database. Experimental results demonstrate the effectiveness of the proposed approach on video with a stochastic textures background with comparisons of the state-of-the-art methods.

Conference Presentation

Citation Download Citation

M. Zhdanova, V. Voronin, E. Semenishchev, Yu. Ilyukhin, and A. Zelensky "Human action recognition with 3D local binary patterns and dense micro-block difference description", Proc. SPIE 11729, Automatic Target Recognition XXXI, 117290H (12 April 2021); https://doi.org/10.1117/12.2588291

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