A method for detecting objects at multiple ranges simultaneously on a path of a vehicle, the method comprising: receiving, by a processor, an image as an input corresponding to objects appearing on the path of the vehicle; splitting, by the processor, the image into a plurality of sub-images indicating region of interest (ROIs), wherein each of the plurality of sub-images is in a form of a rectangular window of pixels computed based on distance of the objects from the vehicle, wherein the distance of the objects from the vehicle is detected based on resolution of the at least one image capturing unit, and wherein a big rectangular window of pixels indicate that the objects are at a short range of distance from the vehicle, and a small rectangular window of pixels indicate that the objects are at a long range of distance from the vehicle; and detecting, by the processor, a first object and a second object at different ranges from the plurality of sub-images by extracting one or more features from the plurality of sub-images; simultaneously processing each of the plurality of sub-images for computing gradient associated with the each of the plurality of sub-images based on the one or more features extracted, wherein processing each of the plurality of sub-images includes: interpolating the one or more sub-images based on the distance of the first object and the second object from the vehicle, wherein the interpolation of the one or more sub-images comprises enlarging size of the one or more sub-images using a bilinear interpolation technique; and applying erosion and dilation upon the one or more sub-images in order to reduce noise from the one or more sub-images, isolating individual elements of the one or more sub-images, and joining of disconnected parts in the one or more sub-images, creating cell histograms comprising plurality of cells by casting weighted vote for an orientation based histogram channel based on values associated with the gradient computed, normalizing the gradients computed by grouping the cells of the plurality of cells in spatial blocks in order to normalize the plurality of sub-images; and applying a Support vector Machine linear classifier on the plurality of sub-images after being normalized in order to detect and classify the first object and the second object in a category. receiving, by a processor, an image as an input corresponding to objects appearing on the path of the vehicle; splitting, by the processor, the image into a plurality of sub-images indicating region of interest (ROIs), wherein each of the plurality of sub-images is in a form of a rectangular window of pixels computed based on distance of the objects from the vehicle, wherein the distance of the objects from the vehicle is detected based on resolution of the at least one image capturing unit, and wherein a big rectangular window of pixels indicate that the objects are at a short range of distance from the vehicle, and a small rectangular window of pixels indicate that the objects are at a long range of distance from the vehicle; and detecting, by the processor, a first object and a second object at different ranges from the plurality of sub-images by extracting one or more features from the plurality of sub-images; simultaneously processing each of the plurality of sub-images for computing gradient associated with the each of the plurality of sub-images based on the one or more features extracted, wherein processing each of the plurality of sub-images includes: interpolating the one or more sub-images based on the distance of the first object and the second object from the vehicle, wherein the interpolation of the one or more sub-images comprises enlarging size of the one or more sub-images using a bilinear interpolation technique; and applying erosion and dilation upon the one or more sub-images in order to reduce noise from the one or more sub-images, isolating individual elements of the one or more sub-images, and joining of disconnected parts in the one or more sub-images, creating cell histograms comprising plurality of cells by casting weighted vote for an orientation based histogram channel based on values associated with the gradient computed, normalizing the gradients computed by grouping the cells of the plurality of cells in spatial blocks in order to normalize the plurality of sub-images; and applying a Support vector Machine linear classifier on the plurality of sub-images after being normalized in order to detect and classify the first object and the second object in a category. interpolating the one or more sub-images based on the distance of the first object and the second object from the vehicle, wherein the interpolation of the one or more sub-images comprises enlarging size of the one or more sub-images using a bilinear interpolation technique; and applying erosion and dilation upon the one or more sub-images in order to reduce noise from the one or more sub-images, isolating individual elements of the one or more sub-images, and joining of disconnected parts in the one or more sub-images, creating cell histograms comprising plurality of cells by casting weighted vote for an orientation based histogram channel based on values associated with the gradient computed, normalizing the gradients computed by grouping the cells of the plurality of cells in spatial blocks in order to normalize the plurality of sub-images; and applying a Support vector Machine linear classifier on the plurality of sub-images after being normalized in order to detect and classify the first object and the second object in a category.