Detecting roadway objects in real-time images
First Claim
1. A method for identifying roadway objects, the method comprising:
- receiving a real-time image of a road from a camera sensor communicatively coupled to an onboard computer of a vehicle;
dividing the real-time image into superpixels;
merging the superpixels to form superpixel regions based on similarity of the superpixels;
generating prior maps from a dataset of road scene images, the prior maps including a road prior map and location data that indicates where certain roadway objects are expected to be located within the real-time image;
drawing a set of bounding boxes where each bounding box surrounds one of the superpixel regions;
comparing bounding boxes in the set of bounding boxes to the road prior map to identify a road region in the real-time image;
determining a proximity of each bounding box in the set of bounding boxes to the road region in the real-time image;
determining a bounding box aspect ratio and a location of each bounding box in the real-time image based on the proximity of each bounding box in the set of bounding boxes to the road region;
comparing the bounding box aspect ratio of each bounding box to a list of approved bounding box aspect ratios for the location based on the prior maps;
pruning bounding boxes from the set of bounding boxes to reduce the set of bounding boxes to a set of remaining bounding boxes when;
the bounding boxes have the bounding box aspect ratio that is inconsistent with the list of approved bounding box aspect ratios for the location based on the bounding box aspect ratio failing to match one or more approved bounding box aspect ratios in the list of approved bounding box aspect ratios for the location, orthe bounding boxes are outside the road region; and
performing feature extraction and categorization to identify objects within the set of remaining bounding boxes based in part on the location data that indicates where the certain roadway objects are expected to be located within the real-time image.
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Abstract
The disclosure includes a method that receives a real-time image of a road from a camera sensor communicatively coupled to an onboard computer of a vehicle. The method includes dividing the real-time image into superpixels. The method includes merging the superpixels to form superpixel regions. The method includes generating prior maps from a dataset of road scene images. The method includes drawing a set of bounding boxes where each bounding box surrounds one of the superpixel regions. The method includes comparing the bounding boxes in the set of bounding boxes to a road prior map to identify a road region in the real-time image. The method includes pruning bounding boxes from the set of bounding boxes to reduce the set to remaining bounding boxes. The method may include using a categorization module that identifies the presence of a road scene object in the remaining bounding boxes.
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Citations
18 Claims
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1. A method for identifying roadway objects, the method comprising:
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receiving a real-time image of a road from a camera sensor communicatively coupled to an onboard computer of a vehicle; dividing the real-time image into superpixels; merging the superpixels to form superpixel regions based on similarity of the superpixels; generating prior maps from a dataset of road scene images, the prior maps including a road prior map and location data that indicates where certain roadway objects are expected to be located within the real-time image; drawing a set of bounding boxes where each bounding box surrounds one of the superpixel regions; comparing bounding boxes in the set of bounding boxes to the road prior map to identify a road region in the real-time image; determining a proximity of each bounding box in the set of bounding boxes to the road region in the real-time image; determining a bounding box aspect ratio and a location of each bounding box in the real-time image based on the proximity of each bounding box in the set of bounding boxes to the road region; comparing the bounding box aspect ratio of each bounding box to a list of approved bounding box aspect ratios for the location based on the prior maps; pruning bounding boxes from the set of bounding boxes to reduce the set of bounding boxes to a set of remaining bounding boxes when; the bounding boxes have the bounding box aspect ratio that is inconsistent with the list of approved bounding box aspect ratios for the location based on the bounding box aspect ratio failing to match one or more approved bounding box aspect ratios in the list of approved bounding box aspect ratios for the location, or the bounding boxes are outside the road region; and performing feature extraction and categorization to identify objects within the set of remaining bounding boxes based in part on the location data that indicates where the certain roadway objects are expected to be located within the real-time image. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. A non-transitory computer-readable medium having computer instructions stored thereon that are executable by a processing device to perform or control performance of steps comprising:
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receiving a real-time image of a road from a camera sensor communicatively coupled to an onboard computer of a vehicle; dividing the real-time image into superpixels; merging the superpixels to form superpixel regions based on similarity of the superpixels; generating prior maps from a dataset of road scene images, the prior maps including a road prior map; drawing a set of bounding boxes where each bounding box surrounds one of the superpixel regions; comparing bounding boxes in the set of bounding boxes to the road prior map to identify a road region in the real-time image; determining a proximity of each bounding box in the set of bounding boxes from the road region in the real-time image; determining a bounding box aspect ratio and a location of each bounding box in the real-time image based on the proximity of each bounding box in the set of bounding boxes to the road region; comparing the bounding box aspect ratio of each bounding box to a list of approved bounding box aspect ratios for the location based on the prior maps; pruning bounding boxes from the set of bounding boxes to reduce the set of bounding boxes to a set of remaining bounding boxes when; the bounding boxes have the bounding box aspect ratio that is inconsistent with the list of approved bounding box aspect ratios for their location based on the bounding box aspect ratio failing to match one or more approved bounding box aspect ratios in the list of approved bounding box aspect ratios for the location, or the bounding boxes are outside the road region; and performing feature extraction and categorization to identify objects within the set of remaining bounding boxes. - View Dependent Claims (13, 14, 15, 16, 17)
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18. A method for identifying roadway objects, the method comprising:
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receiving a real-time image of a road from a camera sensor communicatively coupled to an onboard computer of a vehicle; dividing the real-time image into superpixels; merging the superpixels to form superpixel regions based on similarity of the superpixels; generating prior maps from a dataset of road scene images, the prior maps including a road prior map; drawing a set of bounding boxes where each bounding box surrounds one of the superpixel regions; comparing bounding boxes in the set of bounding boxes to the road prior map to identify a road region in the real-time image; determining a bounding box aspect ratio and a location of each bounding box in the real-time image based on proximity to the road region; comparing the bounding box aspect ratio to a list of approved bounding box aspect ratios for the location based on the prior maps; pruning bounding boxes from the set of bounding boxes; where the bounding box aspect ratio fails to match one of the approved bounding box aspect ratios in the list of approved bounding box aspect ratios for the location outside the road region to reduce the set to remaining bounding boxes, or the bounding boxes are outside the road region; performing feature extraction and categorization to identify objects within the remaining bounding boxes by assigning a type of categorization to each of the remaining bounding boxes based on the bounding box aspect ratio and the location of each bounding box; identifying a presence or an absence of an object within each of the remaining bounding boxes; and determining a type of object based on the feature extraction and categorization.
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Specification