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:
Jan. 04, 2022

Filed:

May. 20, 2019
Applicant:

Tg-17, Llc, Boston, MA (US);

Inventors:

Olga Peled, Petah Tikva, IL;

Yaacob Aizer, Ganei Tikva, IL;

Zcharia Baratz, Tel-Aviv, IL;

Ran Banker, Kfar Saba, IL;

Joseph Keshet, Tel-Aviv, IL;

Ron Asher, Tel-Aviv, IL;

Assignee:

TG-17, Inc., Boston, MA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G08B 13/196 (2006.01); G08B 25/01 (2006.01); G06T 7/20 (2017.01); H04N 13/296 (2018.01); G08B 19/00 (2006.01); G06N 3/08 (2006.01); G05D 1/00 (2006.01); G05D 1/10 (2006.01); B64C 39/02 (2006.01); G06K 9/62 (2006.01); G06K 9/00 (2006.01); G05D 1/12 (2006.01);
U.S. Cl.
CPC ...
G06T 7/20 (2013.01); B64C 39/024 (2013.01); G05D 1/0094 (2013.01); G05D 1/101 (2013.01); G05D 1/12 (2013.01); G06K 9/00369 (2013.01); G06K 9/6259 (2013.01); G06N 3/08 (2013.01); B64C 2201/127 (2013.01); G06T 2207/20084 (2013.01);
Abstract

A goal of the disclosure is to provide real-time adjustment of a deep learning-based tracking system to track a moving individual without using a labeled set of training data. Disclosed are systems and methods for tracking a moving individual with an autonomous drone. Initialization video data of the specific individual is obtained. Based on the initialization video data, real-time training of an input neural network is performed to generate a detection neural network that uniquely corresponds to the specific individual. Real-time video monitoring data of the specific individual and the surrounding environment is captured. Using the detection neural network, target detection is performed on the real-time video monitoring data and a detection output corresponding to a location of the specific individual within a given frame of the real-time video monitoring data is generated. Based on the detection output, first tracking commands are generated to maneuver and center the camera on the location of the specific individual.


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