logo

For the Inventor, By the Inventor

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.

Patent No.:

US 12450878 B1

PDFFull Text
Date of Patent:
Oct. 21, 2025

Filed:

May. 14, 2023

System and method for detecting object in an adaptive environment using a machine learning model

Applicants:

International Institute of Information Technology, Hyderabad, Hyderabad, IN;

Indian Institute of Technology, Delhi, New Delhi, IN;

Indian Institute of Technology, Hyderabad, Sangareddy, IN;

Inventors:

C. V. Jawahar, Hyderabad, IN;

Rohit Saluja, Hyderabad, IN;

Chetan Arora, New Delhi, IN;

Vineeth N Balasubramanian, Sangareddy, IN;

Vaishnavi Mohan Khindkar, Hyderabad, IN;

Assignees:

INTERNATIONAL INSTITUTE OF INFORMATION TECHNOLOGY, HYDERABAD, Hyderabad, IN;

INDIAN INSTITUTE OF TECHNOLOGY, DELHI, New Delhi, IN;

INDIAN INSTITUTE OF TECHNOLOGY, HYDERABAD, Sangareddy, IN;

Attorney:
Primary Examiner:

Henok Shiferaw

Assistant Examiner:

Dennis undefined Rosario

Int. Cl.
CPC ...
G06V 10/77 (2022.01); G06F 18/213 (2023.01); G06F 18/24 (2023.01); G06F 18/25 (2023.01); G06N 3/08 (2023.01); G06N 5/04 (2023.01); G06N 20/00 (2019.01); G06T 5/60 (2024.01); G06T 7/246 (2017.01); G06V 10/20 (2022.01); G06V 10/40 (2022.01); G06V 10/70 (2022.01); G06V 10/74 (2022.01); G06V 10/764 (2022.01); G06V 10/80 (2022.01); G06V 10/82 (2022.01); G06V 20/00 (2022.01); G06V 20/05 (2022.01); G06V 20/10 (2022.01); G06V 20/40 (2022.01); G06V 20/69 (2022.01); G06V 30/19 (2022.01);
U.S. Cl.
CPC ...
G06V 10/7715 (2022.01); G06F 18/213 (2023.01); G06F 18/24 (2023.01); G06F 18/25 (2023.01); G06F 18/251 (2023.01); G06F 18/253 (2023.01); G06F 18/254 (2023.01); G06N 3/08 (2013.01); G06N 5/04 (2013.01); G06N 20/00 (2019.01); G06T 5/60 (2024.01); G06T 7/251 (2017.01); G06V 10/255 (2022.01); G06V 10/40 (2022.01); G06V 10/70 (2022.01); G06V 10/761 (2022.01); G06V 10/764 (2022.01); G06V 10/765 (2022.01); G06V 10/80 (2022.01); G06V 10/806 (2022.01); G06V 10/809 (2022.01); G06V 10/82 (2022.01); G06V 20/00 (2022.01); G06V 20/05 (2022.01); G06V 20/10 (2022.01); G06V 20/35 (2022.01); G06V 20/38 (2022.01); G06V 20/41 (2022.01); G06V 20/47 (2022.01); G06V 20/698 (2022.01); G06V 30/19173 (2022.01);
Abstract

A method for detecting object in an image in a target environment that is adapted to a source environment using a machine learning model is provided. The method includes (i) extracting features from source image associated with source environment and target image associated with target environment, (ii) generating a feature map based on the features, (iii) generating a pixel-wise probability output map (iv) determining a first environment invariant feature map by combining the feature map with the pixel-wise probability output map, (v) determining a second environment invariant feature map by combining the first environment invariant feature map and the features, (vi) generating environment invariant feature maps at different instances, (vii) extracting environment invariant features based on the environment invariant feature maps, (viii) detecting the object in the image in the target environment that is adapted to the source environment by training the machine learning model using the environment invariant features.

Find Patent Forward Citations

Loading…