Multi range object detection device and method

摘要

The present disclosure discloses device and method for detecting objects placed at multiple ranges from vehicle. Images of the objects may be captured by an image capturing unit housed in the vehicle. The image may be splitted into plurality of sub-images. Further, one or more features may be extracted from the plurality of sub-images. Further, each of the plurality of sub-images may be simultaneously processed for computing gradients associated with the plurality of sub-images. Further, a cell histogram may be created by casting weighted vote for an orientation based histogram channel based on values associated with the gradient. The gradients computed may be normalized by grouping the cells in spatial blocks. Further, a Support vector Machine (SVM) linear classifier may be applied on the plurality of sub-images in order to classify the near object and the far object in a category of a pedestrian or a vehicle.

主权项

1. 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 pixel computed based on distance of the objects from the vehicle; and detecting, by the processor, 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 subimages 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.