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:
Dec. 08, 2020

Filed:

Apr. 08, 2019
Applicant:

Uber Technologies, Inc., San Francisco, CA (US);

Inventors:

George Totolos, Jr., Cranberry Township, PA (US);

Joshua Oren Silberman, Redwood City, CA (US);

Daniel Leland Strother, Pittsburgh, PA (US);

Carlos Vallespi-Gonzalez, Pittsburgh, PA (US);

David Bruce Parlour, Pittsburgh, PA (US);

Assignee:

Uber Technologies, Inc., San Francisco, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/62 (2006.01); G06T 7/70 (2017.01); G06T 7/13 (2017.01); B60W 10/18 (2012.01); B60W 10/20 (2006.01); B60W 10/06 (2006.01); B60W 30/09 (2012.01); G06K 9/46 (2006.01); H04N 7/18 (2006.01); G06T 7/73 (2017.01); G06K 9/00 (2006.01); G06T 1/20 (2006.01); G05D 1/00 (2006.01);
U.S. Cl.
CPC ...
G06K 9/6267 (2013.01); B60W 10/06 (2013.01); B60W 10/18 (2013.01); B60W 10/20 (2013.01); B60W 30/09 (2013.01); G06K 9/00805 (2013.01); G06K 9/4609 (2013.01); G06K 9/4642 (2013.01); G06T 1/20 (2013.01); G06T 7/13 (2017.01); G06T 7/70 (2017.01); G06T 7/73 (2017.01); H04N 7/183 (2013.01); H04N 7/188 (2013.01); B60W 2420/42 (2013.01); B60W 2420/52 (2013.01); B60W 2710/0627 (2013.01); B60W 2710/18 (2013.01); B60W 2710/20 (2013.01); G05D 1/0088 (2013.01); G06T 2207/10016 (2013.01); G06T 2207/10024 (2013.01); G06T 2207/10028 (2013.01); G06T 2207/20016 (2013.01); G06T 2207/20076 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30248 (2013.01);
Abstract

Image processing systems can include one or more cameras configured to obtain image data, one or more memory devices configured to store a classification model that classifies image features within the image data as including or not including detected objects, and a field programmable gate array (FPGA) device coupled to the one or more cameras. The FPGA device is configured to implement one or more image processing pipelines for image transformation and object detection. The one or more image processing pipelines can generate a multi-scale image pyramid of multiple image samples having different scaling factors, identify and aggregate features within one or more of the multiple image samples having different scaling factors, access the classification model, provide the features as input to the classification model, and receive an output indicative of objects detected within the image data.


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