US 12,333,739 B2
Machine learning-based re-identification of shoppers in a cashier-less store for autonomous checkout
Michele Toni, Castelnuovo di Garfagnana (IT); Atul Dhingra, Sunnyvale, CA (US); and Jordan Fisher, San Francisco, CA (US)
Assigned to STANDARD COGNITION, CORP., San Francisco, CA (US)
Filed by STANDARD COGNITION, CORP., San Francisco, CA (US)
Filed on Nov. 16, 2022, as Appl. No. 17/988,650.
Application 17/988,650 is a continuation in part of application No. 17/572,590, filed on Jan. 10, 2022, granted, now 11,948,313.
Application 17/572,590 is a continuation of application No. 16/388,765, filed on Apr. 18, 2019, granted, now 11,232,575, issued on Jan. 25, 2022.
Prior Publication US 2023/0088414 A1, Mar. 23, 2023
Int. Cl. G06T 7/246 (2017.01); G06N 20/00 (2019.01)
CPC G06T 7/248 (2017.01) [G06N 20/00 (2019.01); G06T 2207/20081 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method of re-identifying a previously identified subject in an area of real space, the method including:
receiving, from at least two cameras with overlapping fields of view, respective first and second sequences of images of corresponding fields of view in the area of real space;
generating first and second reidentification feature vectors of a first subject identified from a first time interval by performing operations including:
providing first and second images of the first subject from the respective first and second sequences of images and as obtained from the first time interval, to a trained machine learning model to produce respective first and second reidentification feature vectors; and
matching a second subject identified from a second time interval with the first subject identified from the first time interval by performing operations including:
providing third and fourth images of the second subject, from the respective first and second sequences of images and as obtained from the second time interval, to the trained machine learning model to produce respective third and fourth reidentification feature vectors;
calculating (i) a first similarity score between the first and the third reidentification feature vectors and (ii) a second similarity score between the second and the fourth reidentification feature vectors; and
re-identifying the second subject identified from the second time interval as the first subject identified from the first time interval when at least one of the first similarity score and the second similarity score is above a pre-defined threshold.