Paper Info
Title
Dynamic Matrix Decomposition for Action Recognition.
Abstract
Designing a technique for the automatic analysis of different actions in videos in order to detect the presence of interested activities is of high significance nowadays. In this paper, we explore a robust and dynamic appearance technique for the purpose of identifying different action activities. We also exploit a low-rank and structured sparse matrix decomposition (LSMD) method to better model these activities.. Our method is effective in encoding localized spatio-temporal features which enables the analysis of local motion taking place in the video. Our proposed model use adjacent frame differences as the input to the method thereby forcing it to capture the changes occurring in the video. The performance of our model is tested on a benchmark dataset in terms of detection accuracy. Results achieved with our model showed the promising capability of our model in detecting action activities.
Year
Venue
DocType
2019
arXiv: Computer Vision and Pattern Recognition
Journal
Volume
Citations 
PageRank 
abs/1902.07438
0
0.34
References 
Authors
0
1
Name
Order
Citations
PageRank
Abdul Basit102.37