Region of Interest Generation in Dynamic Environments Using Local Entropy Fields

This paper presents a novel technique to generate regions of interest in image sequences containing independent motions. The technique uses a novel motion segmentation method to segment optical flow using a local entropies field. Local entropy values are computed for each optical flow vector and are collected as input for a two state Markov Random Field that is used to discriminate the motion boundaries. Local entropy values are highly informative cues on the amount of information contained in the vector’s neighborhood. High values represent significative motion differences, low values express uniform motions. For each cluster a motion model is fitted and it is used to create a multiple hypothesis prediction for the following frame. Experiments have been performed on standard and outdoor datasets in order to show the validity of the proposed technique.