An original approach for real time detection of changes in motion is presented, which can lead to the detection and recognition of events. Current video change detection focuses on shot changes which depend on appearance, not motion. Changes in motion are detected in pixels that are found to be active via the kurtosis. Statistical modeling of the motion data shows that the Laplace distribution provides the most accurate fit. The Laplace model of the motion is used in a sequential change detection test, which detects the changes in real time. False alarm detection determined whether a detected change is indeed induced by motion or by varying scene illumination. This leads to precise detection of changes in motion for many videos, where shot change detection if shown to fail. Experiments show that the proposed method finds meaningful changes in real time, even under conditions of varying scene illumination.