Detecting roadway objects in real-time images

Patent metadata

Field	Value
Publication number	US-2016267331-A1
Application number	US-201514645936-A
Country	US
Kind code	A1
Filing date	Mar 12, 2015
Priority date	Mar 12, 2015
Publication date	Sep 15, 2016
Grant date	—

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.

First claim

What is claimed is:

1 . A method for identifying roadway objects for a vehicle, the method comprising:
determining, using a camera sensor communicatively coupled to an onboard computer of a vehicle, a real-time image of a road; 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; pruning bounding boxes from the set of bounding boxes that are outside the road region to reduce the set to remaining bounding boxes; and performing feature extraction and categorization to identify objects within the set of remaining bounding boxes.

2 . The method of claim 1 , further comprising providing route guidance based on the identified objects.

3 . The method claim of 1 , wherein merging the superpixels to form superpixel regions based on similarity of the superpixels includes determining texture and similarity of colors of the superpixels.

4 . The method of claim 1 , further comprising:
determining a box area relative to an image resolution of the real-time image for each of the bounding boxes in the set of bounding boxes; and pruning bounding boxes from the set of bounding boxes with box areas that fall below a threshold value to reduce the set to the remaining bounding boxes.

5 . The method of claim 1 , further comprising:
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; and 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 to reduce the set to the remaining bounding boxes.

6 . The method of claim 1 , further comprising:
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, wherein performing feature extraction and categorization comprises applying a type of categorization model 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 bounding boxes in the set of remaining bounding boxes; and determining a type of object based on the feature extraction and categorization.

7 . The method of claim 6 , wherein the type of categorization model includes one or more of a vehicle categorization model, a pedestrian categorization model, a pole categorization model, a bicycle categorization model, a motorcycle categorization model, a stationary bicycle and motorcycle categorization model, and a construction cone categorization model.

8 . The method of claim 1 , further comprising:
determining coordinates and a centroid value for each of the bounding boxes in the set of bounding boxes; comparing the superpixel regions to the road prior map to identify a horizon line and an approximate vanishing point in the real-time image; and pruning bounding boxes from the set of bounding boxes with a centroid value located above the horizon line.

9 . The method of claim 1 , wherein merging the superpixels to form superpixel regions based on similarity of the superpixels is based on using a graph-based agglomerative technique.

10 . The method of claim 1 , wherein the superpixel regions include structures and objects.

11 . The method of claim 1 , wherein the prior maps include locations for objects and structures where the objects and structures include one or more of a vehicle, a construction cone, a pedestrian, a bicycle, a sky, a motorcycle, foliage, a tree, an electrical pole, a streetlight, a road, and a road sign.

12 . The method of claim 1 , further comprising preprocessing the real-time image to remove noise and downsample.

13 . 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:
determining, using a camera sensor communicatively coupled to an onboard computer of a vehicle, a real-time image of a road; 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; pruning bounding boxes from the set of bounding boxes that are outside the road region to reduce the set to remaining bounding boxes; and performing feature extraction and categorization to identify objects within the set of remaining bounding boxes.

14 . The non-transitory computer-readable medium of claim 13 , the steps further comprising providing route guidance based on the identified objects.

15 . The non-transitory computer-readable medium of claim 13 , wherein merging the superpixels to form superpixel regions based on similarity of the superpixels includes determining texture and similarity of colors of the superpixels.

16 . The non-transitory computer-readable medium of claim 13 , the steps further comprising:
determining a box area relative to an image resolution of the real-time image for each of the bounding boxes in the set of bounding boxes; and pruning bounding boxes from the set of bounding boxes with box areas that fall below a threshold value to reduce the set to the remaining bounding boxes.

17 . The non-transitory computer-readable medium of claim 13 , the steps further comprising
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; and 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 to reduce the set to the remaining bounding boxes.

18 . The non-transitory computer-readable medium of claim 13 , the steps further comprising:
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, wherein performing feature extraction and categorization comprises applying a type of categorization model 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 bounding boxes in the set of remaining bounding boxes; and determining a type of object based on the feature extraction and categorization.

19 . The non-transitory computer-readable medium of claim 18 , wherein the type of categorization model includes one or

Assignees

• Toyota Motor Co Ltd

Inventors

• Pillai Preeti Jayagopi
• Yalla Veeraganesh
• Oguchi Kentaro
• Nomoto Hirokazu

Classifications

• G01C21/26 Primary

  specially adapted for navigation in a road network · CPC title

• G06V20/56

  exterior to a vehicle by using sensors mounted on the vehicle · CPC title

• G06V10/50

  by performing operations within image blocks; by using histograms, e.g. histogram of oriented gradients [HoG]; by summing image-intensity values; Projection analysis · CPC title

• G06T2200/04

  involving 3D image data · CPC title

• G06K9/00791 Primary

  Physics · mapped topic