An original approach for real time detection of changes in motion is presented, for detecting and recognizing events. Current video change detection focuses on shot changes, based on appearance, not motion. Changes in motion are detected in pixels that are found to be active, and this motion is input to sequential change detection, which detects changes in real time. Statistical modeling of the motion data shows that the Laplace provides the most accurate fit. This leads to reliable detection of changes in motion for videos where shot change detection is shown to fail. Once a change is detected, the event is recognized based on motion statistics, size, density of active pixels. Experiments show that the proposed method finds meaningful changes, and reliable recognition.