Dynamic Matrix Decomposition for Action Recognition
This work addresses the problem of automatically detecting activities in videos, which is important for surveillance and video analysis, but it appears incremental as it builds on existing matrix decomposition methods.
The paper tackled action recognition in videos by proposing a dynamic appearance technique using low-rank and structured sparse matrix decomposition (LSMD) to model activities, with results showing promising detection accuracy on a benchmark dataset.
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.