1. A method for detecting a target present in at least two images of the same scene acquired simultaneously by different cameras, the method comprising: under development conditions, a prior target-learning step, and; under operating conditions, a simultaneous step of classification of objects present in the images, the target being regarded as detected when an object is classified as being one of the targets learnt during the learning step; wherein the target-learning step includes a step of modeling of the data X corresponding to an area of interest in the images by a parametric distribution law P such that P(X)=P(X 2d ,X 3d ,X T )=P(X 2d )P(X 3d )P(X T ) where X 2d are the luminance data in the area of interest, X 3d are the depth data in the area of interest, and X T are the movement data in the area of interest, the modeling step comprising a step of calculation, in each pixel of the area of interest in one of the two images, of the values dx and dy of the derivatives of the pixel luminance according to two orthogonal directions the luminance data X 2d of the area of interest being modeled by a plurality of α-periodic and/or β-periodic Von Mises-Fischer laws, the periods of which differ from one another, each law describing a distribution of the normal unit vectors on the planes of equation z=dx.x+dv.v corresponding to all of the pixels of the area of interest, P(X 2d ) being obtained by the product of the different Von Mises-Fischer laws modeling the luminance data X 2d .