As far as target tracking is concerned in the robotics field, the picture processing phase consists in finding out the location of the object in the scene within a minimum of time. Therefore, we require quite a new approach of "restrictive correlation" which combines optical function (real time edges extraction) and data processing functions (multilevel correlation). This latter as to show the best matching place between a model of the object and the search area. It consists of a multilevel thresholding followed by a model image mapping at each level. We find out the localization of the object by a weighting addition of each obtained result. The time required to obtain such a result is directly linked to the number of selected points at the thresholding stage.Therefore, we develop an analytical method to count the treated points according to the threshold levels. The grey levels of the picture's points are taken as a realization of a random process of which we measure the statistical characteristics (mean, standard deviation). If we refer to the theory of signal processing, this enables us to determine, by means of calculation, the number of points over a given threshold within an image for a kind of scene. These calculations are carried out for various levels. Afterward, their results are compared with the figures experimentally measured. On this way, we valid a relation which links the execution time of a correlation to its parameters. Consequently, this evaluation gives a quantitative criterion for the values which point out the limits with regard to the choice of thresholds ; the time available for the correlation being previously defined according to the amplitude of the search area and the maximal speed authorized for the target.