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:
Aug. 25, 2020

Filed:

May. 15, 2018
Applicant:

Nec Laboratories America, Inc., Princeton, NJ (US);

Inventors:

Asim Kadav, Jersey City, NJ (US);

Igor Durdanovic, Lawrenceville, NJ (US);

Hans Peter Graf, South Amboy, NJ (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/46 (2006.01); G06K 9/62 (2006.01); G06K 9/66 (2006.01); G06K 9/00 (2006.01); G06N 3/04 (2006.01); G06N 3/08 (2006.01); G06N 5/04 (2006.01);
U.S. Cl.
CPC ...
G06K 9/4628 (2013.01); G06K 9/00624 (2013.01); G06K 9/627 (2013.01); G06K 9/6217 (2013.01); G06K 9/6288 (2013.01); G06K 9/66 (2013.01); G06N 3/04 (2013.01); G06N 3/0445 (2013.01); G06N 3/0454 (2013.01); G06N 3/082 (2013.01); G06N 5/046 (2013.01); G06K 9/0063 (2013.01); G06K 9/00771 (2013.01); G06K 9/00805 (2013.01);
Abstract

Systems and methods for surveillance are described, including an image capture device configured to mounted to an autonomous vehicle, the image capture device including an image sensor. A storage device is included in communication with the processing system, the storage device including a pruned convolutional neural network (CNN) being trained to recognize obstacles in a road according to images captured by the image sensor by training a CNN with a dataset and removing filters from layers of the CNN that are below a significance threshold for image recognition to produce the pruned CNN. A processing device is configured to recognize the obstacles by analyzing the images captured by the image sensor with the pruned CNN and to predict movement of the obstacles such that the autonomous vehicle automatically and proactively avoids the obstacle according to the recognized obstacle and predicted movement.


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