3-D Projected PCA Based DMM Feature Fusing with SMO-SVM for Human Action Recognition

Action recognition in video sequence is a very important and challenging problem yet. This paper presents an efficient feature extraction method for human action recognition for depth video sequence. For the video sequence acquired by depth sensor, all 3-D projections (xy, yz and zx) are calculated for each depth frame. For each projection view, the difference between each alternative frames have been considered to form the Depth Motion Map (DMM). Principle Component Analysis technique is applied to decrease the facet of DMM-feature. Sequential minimal optimization (SMO) is pre-owned to train the Support Vector Machine (SVM). The proposed approach is evaluated on MSR Action-3D data set and compared with the existing approaches. The empirical results convey that proposed approach achieves good results than the existing approaches.