This paper presents a unified approach to human activity capturing and recognition. It targets applications such as a speaker walking, turning around, sitting and getting up from a chair in a classroom setting. A panoramic camera capturing system is designed for video capture. Virtual camera control outputs the region of interest (ROI) video that covers the speaker. Given a ROI sequence, the virtual camera control parameters are used for the recognition of activities like walking, and the motion parameters of each frame are used for the recognition of other activities like turning around, sitting down and getting up etc. For motion parameter based recognition, the likelihood of the motion parameters is represented using a multivariate Gaussian model. The temporal change of the likelihood is characterized using a continuous density hidden Markov model (HMM). Experimental results show that the method works well in recognizing the above mentioned human body activities.