Method for object detection in a digital image, the method comprising the steps of: • receiving at least one pattern representing an object and an input image to be processed; • finding feature points in the at least one pattern and the input image wherein a feature point is a characteristic point including its features that comprise position in the input image (X, Y), scale, rotation, color and contrast data; • for each pattern image matching the pattern's characteristic points against the characteristic points in the input image in order to define points correspondences; the method being characterized in that it further comprises the steps of: (a) considering each points correspondence as a vote, for a particular pattern with a particular position, scale and rotation assuming the same relative position with respect to the coordinates of the pattern center of the characteristic points in the pattern and respective object instance present in the input image, and for each vote determining an adjacency value which denotes a level of match between the corresponding points whereas the votes create a voting space; (b) discarding votes having the adjacency value below a predefined threshold; (c) for each pixel, of the input image, adding the adjacency values of the remaining votes to create a votes image that graphically represents votes in the input image; (d) detecting characteristic points in the votes image as potential propositions for centers of the object instance of a corresponding pattern; (e) sorting the propositions in the votes image according to adjacency sum; (f) for each proposition, starting from the one with the highest adjacency sum, conducting aggregation of votes over an aggregation area centered at the x, y position of the proposition in the voting space; (g) filtering the aggregated votes such that only the highest adjacency vote, corresponding to the same feature point in a pattern is preserved while the other votes are discarded; (h) taking the filtered votes as input to a second pass of steps (f) and (g) of the voting space analysis wherein the input of the second pass creates a location constraint for the aggregation of step (f) by using position, size and rotation of the pattern computed from the filtered votes; (i) executing results consolidation wherein during such consolidation overlapping object detections are searched and only those with the highest adjacency value, of all of the votes the particular object detections were based on, are preserved; (j) taking the chosen detection instance as a new pattern for corresponding object and executing a second pass of the method. • receiving at least one pattern representing an object and an input image to be processed; • finding feature points in the at least one pattern and the input image wherein a feature point is a characteristic point including its features that comprise position in the input image (X, Y), scale, rotation, color and contrast data; • for each pattern image matching the pattern's characteristic points against the characteristic points in the input image in order to define points correspondences; (a) considering each points correspondence as a vote, for a particular pattern with a particular position, scale and rotation assuming the same relative position with respect to the coordinates of the pattern center of the characteristic points in the pattern and respective object instance present in the input image, and for each vote determining an adjacency value which denotes a level of match between the corresponding points whereas the votes create a voting space; (b) discarding votes having the adjacency value below a predefined threshold; (c) for each pixel, of the input image, adding the adjacency values of the remaining votes to create a votes image that graphically represents votes in the input image; (d) detecting characteristic points in the votes image as potential propositions for centers of the object instance of a corresponding pattern; (e) sorting the propositions in the votes image according to adjacency sum; (f) for each proposition, starting from the one with the highest adjacency sum, conducting aggregation of votes over an aggregation area centered at the x, y position of the proposition in the voting space; (g) filtering the aggregated votes such that only the highest adjacency vote, corresponding to the same feature point in a pattern is preserved while the other votes are discarded; (h) taking the filtered votes as input to a second pass of steps (f) and (g) of the voting space analysis wherein the input of the second pass creates a location constraint for the aggregation of step (f) by using position, size and rotation of the pattern computed from the filtered votes; (i) executing results consolidation wherein during such consolidation overlapping object detections are searched and only those with the highest adjacency value, of all of the votes the particular object detections were based on, are preserved; (j) taking the chosen detection instance as a new pattern for corresponding object and executing a second pass of the method.