This paper describes a real-time human action recognition system
that can track multiple persons and recognize distinct human actions through
image sequences acquired from a single fixed camera. In particular, when given
an image, the system segments blobs by using the Mixture of Gaussians
algorithm with a hierarchical data structure. In addition, the system tracks
people by estimating the state to which each blob belongs and assigning people
according to its state. We then make motion history images for tracked people
and recognize actions by using a multi-layer perceptron. The results confirm
that we achieved a high recognition rate for the five actions of walking,
running, sitting, standing, and falling though each subject performed each
action in a slightly different manner. The results also confirm that the proposed
system can cope in real time with multiple persons.