The problem of human activity recognition can be approached using spatio-temporal variations in successive video frames. In this paper, a new human action recognition technique is proposed using multi-view videos. Initially, a naive background subtraction using frame differencing between adjacent frames of a video is performed. Then, the motion information of each pixel is recorded in binary indicating existence/nonexistence of motion in the frame. A pixel wise sum over all the difference images in a view gives the frequency of motion in each pixel throughout the clip. The classification performances are evaluated using these motion frequency features. Our analysis shows that increasing number of views used for feature extraction improves the performance as different views of an activity provide complementary information. Experiments on the i3DPost and the INRIA Xmas Motion Acquisition Sequences (IXMAS) multi-view human action datasets provide significant classification accuracies.