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:
Sep. 03, 2019

Filed:

Sep. 05, 2017
Applicant:

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

Inventors:

Samuel Schulter, Santa Clara, CA (US);

Wongun Choi, Lexington, MA (US);

Paul Vernaza, Sunnyvale, CA (US);

Manmohan Chandraker, Santa Clara, CA (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2006.01); G06T 7/20 (2017.01); G06T 7/77 (2017.01); G06T 7/70 (2017.01); H04N 7/18 (2006.01); G06K 9/62 (2006.01);
U.S. Cl.
CPC ...
G06T 7/20 (2013.01); G06K 9/00771 (2013.01); G06K 9/6274 (2013.01); G06T 7/70 (2017.01); G06T 7/77 (2017.01); H04N 7/18 (2013.01); H04N 7/188 (2013.01); G06T 2207/20076 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30232 (2013.01); G06T 2207/30241 (2013.01);
Abstract

A surveillance system and method are provided. The surveillance system includes at least one camera configured to capture a set of images of a given target area that includes a set of objects to be tracked. The surveillance system includes a memory storing a learning model configured to perform multi-object tracking by jointly learning arbitrarily parameterized and differentiable cost functions for all variables in a linear program that associates object detections with bounding boxes to form trajectories. The surveillance system includes a processor configured to perform surveillance of the target area to (i) detect the objects and track locations of the objects by applying the learning model to the images in a surveillance task that uses the multi-object tracking, and (ii), provide a listing of the objects and their locations for surveillance task. A bi-level optimization is used to minimize a loss defined on a solution of the linear program.


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