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. 21, 2024

Filed:

Jul. 16, 2021
Applicants:

Wipro Limited, Bangalore, IN;

Indian Institute of Science, Bangalore, IN;

Inventors:

Chetan Singh Thakur, Bangalore, IN;

Anirban Chakraborty, Bengaluru, IN;

Sathyaprakash Narayanan, Chennai, IN;

Bibrat Ranjan Pradhan, Bhubaneswar, IN;

Assignees:

Wipro Limited, Bangalore, IN;

Indian Institute of Science, Bangalore, IN;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06T 7/246 (2017.01); B60W 60/00 (2020.01); G06N 3/045 (2023.01); G06V 20/40 (2022.01); G06V 20/56 (2022.01);
U.S. Cl.
CPC ...
B60W 60/001 (2020.02); G06N 3/045 (2023.01); G06T 7/246 (2017.01); G06V 20/40 (2022.01); G06V 20/56 (2022.01); G06T 2207/20084 (2013.01); G06T 2207/30252 (2013.01);
Abstract

A method and activity recognition system for recognising activities in surrounding environment for controlling navigation of an autonomous vehicle is disclosed. The activity recognition system receives first data feed from neuromorphic event-based camera and second data feed from frame-based RGB video camera. The first data feed comprises high-speed temporal information encoding motion associated with change in surrounding environment at each spatial location, and second data feed comprises spatio-temporal data providing scene-level contextual information associated with surrounding environment. An adaptive sampling of second data feed is performed with respect to foreground activity rate based on amount of foreground motion encoded in first data feed. Further, the activity recognition system recognizes activities associated with at least one object in surrounding environment by identifying correlation between both data feed by using two-stream neural network model. Thereafter, based on the determined activities, the activity recognition system controls the navigation of the autonomous vehicle.


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