The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Oct. 22, 2019

Filed:

Mar. 23, 2017
Applicant:

Aptiv Technologies Limited, St. Michael, BB;

Inventors:

Stephanie Lessmann, Erkrath, DE;

Mirko Meuter, Erkrath, DE;

Jens Westerhoff, Dortmund, DE;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 20/00 (2019.01); G06K 9/62 (2006.01); G06K 9/66 (2006.01); G06T 5/20 (2006.01); G06K 9/00 (2006.01); G06T 7/73 (2017.01); G06T 7/269 (2017.01);
U.S. Cl.
CPC ...
G06N 20/00 (2019.01); G06K 9/00791 (2013.01); G06K 9/6262 (2013.01); G06K 9/6267 (2013.01); G06K 9/66 (2013.01); G06T 5/20 (2013.01); G06T 7/269 (2017.01); G06T 7/73 (2017.01); G06T 2207/10016 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30244 (2013.01); G06T 2207/30252 (2013.01);
Abstract

A method of generating a confidence measure for an estimation derived from images captured by a camera mounted on a vehicle includes: capturing consecutive training images by the camera while the vehicle is moving; determining ground-truth data for the training images; computing optical flow vectors from the training images and estimating a first output signal based on the optical flow vectors for each of the training images, the first output signal indicating an orientation of the camera; classifying the first output signal for each of the training images as a correct signal or a false signal depending on how good the first output signal fits to the ground-truth data; determining optical flow field properties for each of the training images derived from the training images; and generating a separation function that separates the optical flow field properties into two classes based on the classification of the first output signal.


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