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:
May. 19, 2020

Filed:

Mar. 21, 2017
Applicant:

Tata Consultancy Services Limited, Mumbai, IN;

Inventors:

Ehtesham Hassan, Gurgaon, IN;

Gaurav Gupta, Gurgaon, IN;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2006.01); G06T 7/70 (2017.01); G06T 7/11 (2017.01); G06T 7/246 (2017.01); G06T 7/12 (2017.01); G06K 9/32 (2006.01); G06T 7/90 (2017.01); G06F 3/01 (2006.01); G06K 9/46 (2006.01); G06K 9/62 (2006.01); G06K 9/66 (2006.01); G06T 3/40 (2006.01); G06T 5/20 (2006.01);
U.S. Cl.
CPC ...
G06T 7/70 (2017.01); G06F 3/012 (2013.01); G06K 9/00234 (2013.01); G06K 9/3216 (2013.01); G06K 9/4604 (2013.01); G06K 9/6267 (2013.01); G06K 9/66 (2013.01); G06T 3/40 (2013.01); G06T 5/20 (2013.01); G06T 7/11 (2017.01); G06T 7/12 (2017.01); G06T 7/248 (2017.01); G06T 7/90 (2017.01); G06K 2009/3225 (2013.01); G06K 2009/3291 (2013.01); G06T 2207/10024 (2013.01); G06T 2207/20061 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30204 (2013.01);
Abstract

Systems and methods for detecting and tracking a marker in real time is disclosed. Shape based segmentation of at least one object detected in a first frame from a sequence of frames is performed to define a region of interest (ROI) surrounding an object of interest corresponding to the marker. A marker detection model is dynamically trained based on sampling points from a plurality of pixels in and around the ROI. The marker is then tracked in real-time based on projected ROI in subsequent frames and the trained marker detection model. To optimize computation time required in classifying the pixels as marker pixels or non-marker pixels, the ROI is reduced to half its size, classification is performed on the reduced ROI and to improve accuracy, blob detection and classifying pixels along the boundary of the reduced ROI is performed by processing the ROI in original resolution.


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