Anyvision Interactive Technologies Ltd.

Ailon Etshtein

Ishay Sivan

Sankha Subhra Mukherjee

Rolf Hugh Baxter

Neil Martin Robertson

Alexander Zilberman

Ohad Shaubi

Idan Barak

Yaaqov Valero

Presented herein are systems, methods and apparatuses for increasing reliability of face recognition in analysis of images captured by drone mounted imaging sensors, comprising: recognizing a target person in one or more iterations, each iteration comprising: identifying one or more positioning properties of the target person based on analysis of image(s) captured by imaging sensor(s) mounted on a drone operated to approach the target person, instructing the drone to adjust its position to an optimal facial image capturing position selected based on the positioning property(s), receiving facial image(s) of the target person captured by the imaging sensor(s), receiving a face classification associated with a probability score from machine learning model(s) trained to recognize the target person, and initiating another iteration in case the probability score does not exceed a certain threshold. Finally, the face classification may be outputted for use by one or more face recognition based systems.

Adaptive positioning of drones for enhanced face recognition

Provided herein are systems and methods for applying adaptive classes thresholds to enhance object detection Machine Learning (ML) models by receiving a plurality of labeled feature vectors extracted from a plurality of images associated with a plurality of objects, one or more subsets of the plurality of feature vectors are associated with respective object(s) and labeled accordingly, computing an adaptive threshold for each object in a plurality of iterations, each iteration comprising: (1) computing deviation of a respective feature vector of the subset from an aggregated feature vector, (2) computing, in case the deviation is within a predefined value, a threshold enclosing the respective feature vector, and (3) adjusting the adaptive threshold to enclose the threshold of the respective feature vector and outputting the adaptive threshold(s) for classifying unlabeled feature vectors to class(s) of respective object(s) associated with the adaptive threshold(s) in which the unlabeled feature vectors fall.

Inter-class adaptive threshold structure for object detection

Presented herein are systems and methods for increasing reliability of object detection, comprising, receiving a plurality of images of one or more objects captured by imaging sensor(s), receiving an object classification coupled with a first probability score from machine learning model(s) trained to detect the object(s) and applied to the image(s), computing a second probability score for classification of the object(s) according to physical attribute(s) of the object(s) estimated by analyzing the image(s), computing a third probability score for classification of the object(s) according to a movement pattern of the object(s) estimated by analyzing at least some consecutive images, computing an aggregated probability score aggregating the first, second and third probability scores, and outputting, in case the aggregated probability score exceeds a certain threshold, the classification of each object coupled with the aggregated probability score for use by object detection based system(s).

Object detection based on machine learning combined with physical attributes and movement patterns detection

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Tel Aviv, Israel

Ailon Etshtein