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Action recognition using the 3D dense microblock difference

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publicationCounterterrorism, Crime Fighting, Forensics, and Surveillance Technologies II
PublisherSPIE
ISBN (Electronic)9781510621879
DOIs
Publication statusPublished - 2018
Publication typeA4 Article in a conference publication
EventCounterterrorism, Crime Fighting, Forensics, and Surveillance Technologies - Berlin, Germany
Duration: 10 Sep 201811 Sep 2018

Publication series

NameProceedings of SPIE
Volume10802
ISSN (Electronic)1996-756X

Conference

ConferenceCounterterrorism, Crime Fighting, Forensics, and Surveillance Technologies
CountryGermany
CityBerlin
Period10/09/1811/09/18

Abstract

This paper describes a framework for action recognition which aims to recognize the goals and activities of one or more human from a series of observations. We propose an approach for the human action recognition based on the 3D dense micro-block difference. The proposed algorithm is a two-stage procedure: (a) image preprocessing using a 3D Gabor filter and (b) a descriptor calculation using 3D dense micro-block difference with SVM classifier. At the first step, an efficient spatial computational scheme designed for the convolution with a bank of 3D Gabor filters is present. This filter intensifies motion using a convolution for a set of 3D patches and arbitrarily-oriented anisotropic Gaussian. For preprocessed frames, we calculate the local features such as 3D dense micro-block difference (3D DMD), which capture the local structure from the image patches at high scales. This approach is processing the small 3D blocks with different scales from frames which capture the microstructure from it. The proposed image representation is combined with fisher vector method and linear SVM classifier. We evaluate the proposed approach on the UCF50, HMDB51 and UCF101 databases. 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.

Keywords

  • 3D Gabor filter., Action recognition, Micro-block difference, Texture

Publication forum classification

Field of science, Statistics Finland