Foreground object localization using a flooding algorithm based on inter-frame change and colour

A Bayesian, fully automatic moving object localization method is proposed, using inter-frame differences and background/foreground colour as discrimination cues. Change detection pixel classification to one of the labels "changed" or "unchanged" is obtained by mixture analysis, while histograms are used for statistical description of colours. High confidence, change detection based, statistical criteria are used to compute a map of initial labelled pixels. Finally, a region growing algorithm, which is named priority multi-label flooding algorithm, assigns pixels to labels using Bayesian dissimilarity criteria. Localization results on well-known benchmark image sequences as well as on webcam and compressed videos are presented.