Method for motion estimation between two images of an environmental region of a motor vehicle, computing device, driver assistance system as well as motor vehicle

Patent metadata

Field	Value
Publication number	US-10567704-B2
Application number	US-201716084512-A
Country	US
Kind code	B2
Filing date	Mar 10, 2017
Priority date	Mar 15, 2016
Publication date	Feb 18, 2020
Grant date	Feb 18, 2020

Abstract

The invention relates to a method for motion estimation between two images of an environmental region (9) of a motor vehicle (1) captured by a camera (4) of the motor vehicle (1), wherein the following steps are performed: a) determining at least two image areas of a first image as at least two first blocks (B) in the first image, b) for each first block (B), defining a respective search region in a second image for searching the respective search region in the second image for a second block (B) corresponding to the respective first block (B); c) determining a cost surface (18) for each first blocks (B) and its respective search region; d) determining an averaged cost surface (19) for one of the at least two first blocks (B) based on the cost surfaces (18); d) identifying a motion vector (v) for the one of the first blocks (B) describing a motion of a location of the first block (B) in the first image and the corresponding second block (B) in the second image. The invention also relates to a computing device (3), a driver assistance system (2) as well as a motor vehicle (1).

First claim

The invention claimed is:

1. A method for motion estimation between two images of an environmental region of a motor vehicle captured by a camera of the motor vehicle, the method comprising:
determining at least two image areas of a first image as at least two first blocks in a block grid in the first image;
for each first block, defining a respective search region in a second image for searching the respective search region in the second image for a second block corresponding to the respective first block using block matching;
determining a cost surface for each first blocks and its respective search region;
determining an averaged cost surface for one of the at least two first blocks based on the cost surfaces; and
identifying a motion vector for the one of the first blocks describing a motion of a location of the first block in the first image and the corresponding second block in the second image.

2. The method according to claim 1 , wherein a global minimum of the averaged cost surface is determined and the motion vector is determined in dependency on the global minimum.

3. The method according to claim 1 , wherein for determining the average cost surface, a mean value of each cost surface is determined, and respective weighting factors for determining the averaged cost surface are determined based on the mean values.

4. The method according to claim 3 , wherein the weighting factor is determined as a reciprocal of the respective mean value.

5. The method according to claim 1 , wherein a sliding window is determined comprising a predetermined number of first blocks, wherein the motion vector is determined for one of the first blocks within the sliding window based on the cost surfaces of all first blocks within the sliding window.

6. The method according to claim 5 , wherein the number of first blocks within the sliding window is preset such that one first block is completely surrounded by further first blocks within the sliding window, wherein the motion vector is determined for the first block in the middle surrounded by the further first blocks.

7. The method according to claim 1 , wherein an extrinsic calibration of the camera is performed based on the motion vector derived from the averaged cost surface.

8. The method according to claim 7 , wherein for performing the extrinsic calibration, a rotation calibration of the camera is performed, wherein a loss function describing a deviation between the motion vector and a predetermined vector is determined and a rotation-compensated motion vector is determined by minimizing the loss function.

9. The method according to claim 8 , wherein for performing the extrinsic calibration, a height calibration of the camera is performed, wherein the height of the camera is determined in dependency on a length of the rotation-compensated motion vector and an expected value of the length of the rotation-compensated motion vector.

10. The method according to claim 9 , wherein the expected value for the length is preset in dependency on a velocity of the motor vehicle.

11. The method according to claim 10 , wherein the velocity of the motor vehicle is determined by means of odometry and/or based on at least one further motion vector determined for at least one further camera.

12. A computing device for a driver assistance system of a motor vehicle, which is adapted to perform a method according to claim 1 .

13. A driver assistance system for a motor vehicle comprising at least one camera and a computing device according to claim 12 .

14. A motor vehicle with a driver assistance system according to claim 13 .

Assignees

• Connaught Electronics Ltd

Inventors

• Ermilios Pantelis
• Mcnamara Bryan
• O'Grady William
• Friel Myles

Classifications

• G06T7/223 Primary

  using block-matching · CPC title

• G06T2207/20021

  Dividing image into blocks, subimages or windows · CPC title

• G06T7/80 Primary

  Analysis of captured images to determine intrinsic or extrinsic camera parameters, i.e. camera calibration · CPC title

• G06T2207/30252

  Vehicle exterior; Vicinity of vehicle · CPC title

• G06T2207/10016

  Video; Image sequence · CPC title