Paper Info
Title
Multi-resolution approach to human activity recognition in video sequence based on combination of complex wavelet transform, Local Binary Pattern and Zernike moment
Abstract
Human activity recognition is a challenging problem of computer vision and it has different emerging applications. The task of recognizing human activities from video sequence exhibits more challenges because of its highly variable nature and requirement of real time processing of data. This paper proposes a combination of features in a multiresolution framework for human activity recognition. We exploit multiresolution analysis through Daubechies complex wavelet transform (DCxWT). We combine Local binary pattern (LBP) with Zernike moment (ZM) at multiple resolutions of Daubechies complex wavelet decomposition. First, LBP coefficients of DCxWT coefficients of image frames are computed to extract texture features of image, then ZM of these LBP coefficients are computed to extract the shape feature from texture feature for construction of final feature vector. The Multi-class support vector machine classifier is used for classifying the recognized human activities. The proposed method has been tested on various standard publicly available datasets. The experimental results demonstrate that the proposed method works well for multiview human activities as well as performs better than some of the other state-of-the-art methods in terms of different quantitative performance measures.
Year
DOI
Venue
2022
10.1007/s11042-021-11828-6
Multimedia Tools and Applications
Keywords
DocType
Volume
Daubechies complex wavelet transform, Human activity recognition, Local Binary Pattern, Multiresolution analysis, Video surveillance, Zernike moment
Journal
81
Issue
ISSN
Citations 
24
1380-7501
0
PageRank 
References 
Authors
0.34
29
2
Name
Order
Citations
PageRank
m khare100.34
Moongu Jeon245672.81