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

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.