We address the issue of forming a pre-attentive mechanism that can be used to analyse surveillance sequences. We address the problem of spotting scene change by performing temporal segmentation on long video sequences with little colour information and observed content. This is typical in surveillance sequences. Our approach: (1) employs sustained temporal change computed for local neighbourhoods in the image frames; (2) defines a frame activity similarity metric that accounts for local spatial and temporal displacement of change; and (3) monitors the similarity over a wide period to detect changes in emphasis that are then identified as scene breaks.