Parametric improvement of lateral interaction in accumulative computation in motion-based segmentation

Segmentation of moving objects is an essential component of any vision system. However, its accomplishment is hard due to some challenges such as the occlusion treatment or the detection of objects with deformable appearance. In this paper an artificial neuronal network approach for moving object segmentation, called lateral interaction in accumulative computation (LIAC), which uses accumulative computation and recurrent lateral interaction is revisited. Although the results reported for this approach so far may be considered relevant, the problems faced each time (environment, objects of interest, etc.) make that the system outcome varies. Hence, our aim is to improve segmentation provided by LIAC in a double sense: by removing the detected objects not matching some size or compactness constraints, and by learning suitable parameters that improve the segmentation behavior through a genetic algorithm.