Exploiting the Motion Learning Paradigm for Recognizing Human Actions

Identifying the human actions in unconfined videos is a difficult problem in several applications. The human action recognition is an active area of research. For this purpose the system proposes motion representation. In many proposed methods the motion pattern was avoided. So here these motion relationships which were discarded previously are now proposed. Identifying the actions in videos using motion scheme is been proposed. The video event representation used to recognize human activities is based on motion modeling. Dense local patch trajectories such as Histogram of Optical Flow (HOF), Histogram of Oriented Gradients (HOG) and Motion Boundary Histograms (MBH) areused in this approach which does not require the background foreground separation. The dimensions are reduced by Principal Components Analysis (PCA). The Support Vector Machine (SVM) is the classifier used for classification. The proposed video representation model is applied on the UT-Interaction dataset. The experimental results show that proposed representation produces a very competitive performance when compared with state-of-the-art methods and is more accurate.