In general automatic activity-recognition maybe defined as a computer-vision system that recognize what activities are occurring in a video-monitored environment, from a predefined set of activities to look for.  It is a cutting-edge research-field that will potentially allow for lots of interesting applications, such as, automatic video-monitoring, robust surveillance, and much more.

However instead of detecting what activities are occurring in an environment, we are interested in creating a system that will learn the various ways a single predefined-activity may occur from a limited amount of visual data of the activity. 

Using this information, the system will classify a new instance of the activity as either belonging to one of the many variations of the activity or as an abnormally. Currently we are interested in developing a system to learn the variations in a loading dock activity, and to detect when an abnormally occurs.