Human Motion Classification Based on Global Representation and Conditional Model

This paper presents a new classification method for single subject’s motion. We employ R transform descriptor and Linear-chain Conditional Random Fields for representation and classification. What it solves is that global features are described and adjacent states are independent. We extract binary silhouettes from a video sequence and segment them into groups by cycle after building the background model within Gaussian mixture model. Then low-level features are represented by R transform and principal vectors are obtained by Principal Component Analysis. After forming these vectors into a matrix, we utilize Linear-chain Conditional Random Fields to train and classify these groups, and demonstrate its usability in practice. Compared with the state-of-the-arts, our approach is simple in motion representation and independent between adjacent frames, and recognition accuracy improves to about 96% in average. It can be implemented in video surveillance where the background is known.