Detector-tracker architecture

A machine-learning (ML) architecture may comprise a first ML model and/or an optical flow model that receive, as input, a first image and a second image. The first ML model may output a first feature map corresponding to the first image and a second feature map corresponding to the second image. The...

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Bibliographic Details
Main Authors Tariq, Sarah, Tan, Qijun
Format Patent
LanguageEnglish
Published 03.11.2020
Subjects
CALCULATING
COMPUTING
CONTROLLING
COUNTING
HANDLING RECORD CARRIERS
IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
PHYSICS
PRESENTATION OF DATA
RECOGNITION OF DATA
RECORD CARRIERS
REGULATING
SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
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Summary:A machine-learning (ML) architecture may comprise a first ML model and/or an optical flow model that receive, as input, a first image and a second image. The first ML model may output a first feature map corresponding to the first image and a second feature map corresponding to the second image. The optical flow model may output an estimated optical flow. A deformation component may modify the second feature map, as a deformed feature map, based at least in part on the estimated optical flow. The deformed feature map and the first feature map may be concatenated together as a concatenated feature map, which may be provided to a second ML model. The second ML model may be trained to output an output ROI and/or a track in association with an object represented in the first image.
Bibliography:Application Number: US201916297381