Multi range object detection device and method

A device (102) for detecting objects at multiple ranges simultaneously on a path of a vehicle, the device (102) comprising:

• a processor (202);
• a memory (206) coupled with the processor (202), wherein the processor(202) executes a plurality of modules (208) stored in the memory (206), and wherein the plurality of modules 208 comprises:
• a receiving module (210) to receive an image as an input corresponding to objects appearing on the path of the vehicle;
• a splitting module (212) to split 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;
• and
• a detecting module (214) to detect a first object and a second object 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;
• 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 (SVM) 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.
• sharpening the one or more sub-images using a two dimensional image sharpening filter with a median of 3×3 kernels, and
• smoothening the one or more sub-images using two dimensional smoothening filters with 3×3 Gaussian kernel.

A non-transitory computer readable medium embodying a program executable in a computing device for detecting objects at multiple ranges simultaneously on a path of a vehicle, the program comprising:

• a program code for receiving an image as an input corresponding to objects appearing on the path of the vehicle;
• a program code for splitting 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;
• and
• a program code for detecting a first object and a second object 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
• 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;
• 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 (SVM) 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.
• sharpening the one or more sub-images using a two dimensional image sharpening filter with a median of 3×3 kernels, and
• smoothening the one or more sub-images using two dimensional smoothening filters with 3×3 Gaussian kernel.