This ``hard to describe" but ``easy to verify" property of unusual events suggests an intuitive two-step solution for their detection. In the first step, one extracts image features from the video, typically achieved by detecting and tracking moving objects . From tracked objects trajectory, speed, and possibly the shape descriptor of the moving objects can be computed . In the second step the extracted features are used to develop models for the ``normal'' activities, either by hand or by applying supervised machine learning techniques . A common choice is to use Hidden Markov Models [1,9,10] or other graphical models  which quantize image features into a set of discrete states and model how states change in time. In order to detect unusual events the video is matched against a set of normal models and segments which do not fit the models is considered unusual.