Apple Inc.

Other

Esg Elektroniksystem- Und Logistik Gmbh

Rohde and Schwarz Gmbh & Co. Kg

Thomas Gebauer

Raffi A Bedikian

Daniel R Kurz

Li Jia

Branko Petljanski

Qian Wang

Juri Platonov

Muhammad Ahmed Riaz

Dewey H Lee

Christopher Pflug

Pawel Kaczmarczyk

Christoph Krall

In one implementation, a method includes emitting light with modulating intensity from a plurality of light sources towards an eye of a user. The method includes receiving light intensity data indicative of an intensity of the plurality of glints reflected by the eye of the user in the form of a plurality of glints. The method includes determining an eye tracking characteristic of the user based on the light intensity data. In one implementation, a method includes generating, using an event camera comprising a plurality of light sensors at a plurality of respective locations, a plurality of event messages, each of the plurality of event messages being generated in response to a particular light sensor detecting a change in intensity of light and indicating a particular location of the particular light sensor. The method includes determining an eye tracking characteristic of a user based on the plurality of event messages.

Method and device for eye tracking using event camera data

One implementation involves a device receiving a stream of pixel events output by an event camera. The device derives an input image by accumulating pixel events for multiple event camera pixels. The device generates a gaze characteristic using the derived input image as input to a neural network trained to determine the gaze characteristic. The neural network is configured in multiple stages. The first stage of the neural network is configured to determine an initial gaze characteristic, e.g., an initial pupil center, using reduced resolution input(s). The second stage of the neural network is configured to determine adjustments to the initial gaze characteristic using location-focused input(s), e.g., using only a small input image centered around the initial pupil center. The determinations at each stage are thus efficiently made using relatively compact neural network configurations. The device tracks a gaze of the eye based on the gaze characteristic.

Event camera-based gaze tracking using neural networks

The present disclosure describes techniques for training a neural network such that the trained network can be implemented to perform a utility task (e.g., a classification task) while inhibiting performance of a secondary task (e.g., a privacy-violating task). In some embodiments, the techniques include training a neural network using a first loss associated with a first task and a second loss associated with a second task different from the first task. In some embodiments, this includes performing a first training operation associated with the first loss, and performing a second training operation associated with the second loss, wherein the second training operation includes providing, to the neural network, a plurality of input items associated with the second task.

Training a network to inhibit performance of a secondary task

In one implementation, a method includes emitting light with modulating intensity from a plurality of light sources towards an eye of a user. The method includes receiving light intensity data indicative of an intensity of the plurality of glints reflected by the eye of the user in the form of a plurality of glints and determining an eye tracking characteristic of the user based on the light intensity data. In one implementation, a method includes generating, using an event camera comprising a plurality of light sensors at a plurality of respective locations, a plurality of event messages, each of the plurality of event messages being generated in response to a particular light sensor detecting a change in intensity of light and indicating a particular location of the particular light sensor. The method includes determining an eye tracking characteristic of a user based on the plurality of event messages.

A method for determining the orientation of a video camera attached to a vehicle relative to the vehicle coordinate system is disclosed. Using a video camera an incremental motion is measured based on the optical flow of the video stream. A linear motion component and a rotational motion component are determined. Based on a determined rotational angle the incremental motion is classified as linear or as rotational. The directional vector of the linear motion is used to estimate the longitudinal axis of the vehicle, and the rotational vector of the rotational motion is used for estimating the normal to the vehicle plane. Based thereon the orientation of the camera follows.

Method and device for online calibration of vehicle cameras

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Munich, Germany

Thomas Gebauer