Motion detection in an image sequence using Gibbs distributions

The authors address the problem of motion detection in an image sequence from the variations in time of the intensity distribution. The problem is not limited to change detection but encompasses the recovery of the projections of moving areas in the image. The approach is characterized by the joint treatment of the detection of temporal changes and the reconstruction of mobile object masks according to a probabilistic formulation. More formally, spatio-temporal contextual information is introduced through Markovian models, using Gibbs distributions defined on a spatio-temporal neighborhood system. Then the problem at hand is stated as a statistical labeling one. To decide whether or not a point belongs to a moving area is equivalent to assigning to it a given label. A solution to this labeling problem is formulated according to the maximum a posteriori (MAP) criterion. Experiments with a real image sequence have been carried out