An image processing method for lane classification, comprising: determining a candidate track in an image of a road, wherein the candidate track (530, 540) is modelled as a parameterized line or curve passing through a candidate lane marking (510, 520, 610, 620) in the image of a road (200, 500); dividing the candidate track into a plurality of cells, each cell (C1-C7) corresponding to a segment of the candidate track (530, 540); for each of a plurality of said cells determining the highest possible number of marklets, wherein each marklet of a cell (C1-C7) corresponds to a line or curve connecting left and right edges of the candidate lane marking (510, 520, 610, 620) comprised in the cell, wherein the marklets are distributed in a direction extending along the candidate lane markings (510, 520, 610, 620); determining at least one local feature of each of said plurality of cells (C1-C7) based on characteristics of said marklets of each respective cell (C1-C7); determining at least one global feature of the candidate track by aggregating the local features of the plurality of cells (C1-C7); and determining if the candidate lane marking (510, 520, 610, 620) represents a lane marking based on the at least one global feature; wherein if it is determined that the candidate lane marking (510, 520, 610, 620) represents a lane marking, the lane marking is classified as a solid line or as a dashed line based on the local features of a plurality of said cells (C1-C7); wherein said determining at least one local feature includes determining at least one inlier ratio of at least one of the cells (C1-C7) as the ratio between the number of marklets determined for the cell (C1-C7) and the number of image pixel lines comprised by the cell (C1-C7), and wherein the lane marking is classified as a solid line or as a dashed line based on the determined at least one inlier ratio; wherein the aggregating of the local features and the determining if the candidate lane marking represents a lane marking based on the respective global feature are performed using a machine learning classifier. determining a candidate track in an image of a road, wherein the candidate track (530, 540) is modelled as a parameterized line or curve passing through a candidate lane marking (510, 520, 610, 620) in the image of a road (200, 500); dividing the candidate track into a plurality of cells, each cell (C1-C7) corresponding to a segment of the candidate track (530, 540); for each of a plurality of said cells determining the highest possible number of marklets, wherein each marklet of a cell (C1-C7) corresponds to a line or curve connecting left and right edges of the candidate lane marking (510, 520, 610, 620) comprised in the cell, wherein the marklets are distributed in a direction extending along the candidate lane markings (510, 520, 610, 620); determining at least one local feature of each of said plurality of cells (C1-C7) based on characteristics of said marklets of each respective cell (C1-C7); determining at least one global feature of the candidate track by aggregating the local features of the plurality of cells (C1-C7); and determining if the candidate lane marking (510, 520, 610, 620) represents a lane marking based on the at least one global feature; wherein if it is determined that the candidate lane marking (510, 520, 610, 620) represents a lane marking, the lane marking is classified as a solid line or as a dashed line based on the local features of a plurality of said cells (C1-C7); wherein said determining at least one local feature includes determining at least one inlier ratio of at least one of the cells (C1-C7) as the ratio between the number of marklets determined for the cell (C1-C7) and the number of image pixel lines comprised by the cell (C1-C7), and wherein the lane marking is classified as a solid line or as a dashed line based on the determined at least one inlier ratio; wherein the aggregating of the local features and the determining if the candidate lane marking represents a lane marking based on the respective global feature are performed using a machine learning classifier.