A Background Model Initialization Algorithm for Video Surveillance

Many motion detection and tracking algorithms rely on the process of background subtraction, a technique which detects changes from a model of the background scene. We present a new algorithm for the purpose of background model initialization. The algorithm takes as input a video sequence in which moving objects are present, and outputs a statistical background model describing the static parts of the scene. Multiple hypotheses of the background value at each pixel are generated by locating periods of stable intensity in the sequence. The likelihood of each hypothesis is then evaluated using optical ow information from the neighborhood around the pixel, and the most likely hypothesis is chosen to represent the background. Our results are compared with those of several standard background modeling techniques using surveillance video of humans in indoor environments