RELATED APPLICATIONS

This application is a national stage entry under 35 U.S.C. 371 of PCT/AU2019/051056, filed Sep. 30, 2019, which in turn claims priority to Australian Patent Application No. 2018903678, filed Sep. 28, 2018, the disclosures of which are incorporated herein by reference.

FIELD

This disclosure relates to monitoring systems and to a method and system for estimating an attention state of a subject being monitored, such as a vehicle driver. While some embodiments will be described herein with reference to that application, it will be appreciated that the disclosure is not limited to such a field of use, and is applicable in broader contexts.

BACKGROUND

Any discussion of the background throughout the specification should in no way be considered as an admission that such background information is widely known or forms part of common general knowledge in the field.

Driver monitoring systems rely on estimating driver head pose and eye gaze direction. However, for these parameters to be useful, it is necessary to accurately know the scene geometry to identify where the driver's attention is projected. Raw gaze and head pose data are quite noisy and are associated with varying degrees of uncertainty. This uncertainty leads to errors in the estimation of a driver's attention state.

The inventor has identified a desire to more accurately or efficiently estimate an attention state of a vehicle driver or other subject being monitored.

SUMMARY

In accordance with a first aspect of the disclosure there is provided a method of estimating an attention state of a subject within a scene using a monitoring system, the monitoring system including an imaging camera to capture images of the subject and generate subject attention data of the subject, the system configured to perform operations including:a) generating a primary visual attention ray of the subject from current subject attention data;b) generating an angular distribution of visual attention rays having an origin common to the primary visual attention ray;c) projecting the primary visual attention ray and the angular distribution of visual attention rays onto a digital representation of the scene having a plurality of predefined regions of interest to thereby generate projected visual attention rays;d) determining an intersection of the projected visual attention rays with one or more of the regions of interest; ande) based on the intersection, determining an estimated attention state of the subject's attention.

In some embodiments, the subject attention data includes one or both of eye gaze and head pose data.

In some embodiments, the method includes the initial stage of determining an availability of current subject attention data. If current eye gaze data is available, the primary visual attention ray is generated based on the current eye gaze data. If current eye gaze data is not available, the primary visual attention ray is generated based on current head pose data.

In some embodiments, the method includes the stage of determining a confidence measure of visual attention. In some embodiments, the angular distribution of visual attention rays is distributed based, at least in part, on the confidence measure. In some embodiments, the confidence measure is based on the angle of the primary visual attention ray relative to a position and/or orientation of the imaging camera.

In some embodiments, stage e) includes determining a number of intersections of the projected visual attention rays with the one or more regions of interest. The estimated attention state may include a likely region of interest of the subject's attention. In some embodiments, the likely region of interest of the subject's attention is designated as the region of interest which has the most intersecting visual attention rays.

In some embodiments, stage e) further includes applying a weighting to particular regions of interest. The weighting may be applied dynamically. The weighting may be based on recent subject attention behavior. Thus, in some embodiments, the likely region of interest of the subject's attention is determined as the region of interest having the highest value based on a weighted sum of intersections of the visual attention rays with the one or more regions of interest. The weighting may be applied based on the size of the region of interest.

The estimated attention state may include a designation that the subject's attention is outside the one or more regions of interest.

In some embodiments, the distribution of visual attention rays is a gaussian distribution. In some embodiments, the distribution has a standard deviation based on a customary ocular motor range of a human. In some embodiments, the distribution has a standard deviation based on a real-time estimate of the confidence measure. The confidence measure may include an RMS error of the visual attention ray.

In accordance with a second aspect of the disclosure there is provided a monitoring system configured to perform a method according to the first aspect.

In accordance with a third aspect of the disclosure there is provided a system for estimating an attention state of a subject within a scene, the system including:an imaging camera to capture digital images of the subject;one or more light sources configured to illuminate the subject during a period in which the digital images are captured; anda processor configured to:process the captured images and generate subject attention data of the subject;generate a primary visual attention ray of the subject from current subject attention data;generate an angular distribution of visual attention rays having an origin common to the primary visual attention ray;project the visual attention rays onto a digital representation of the scene having a plurality of predefined regions of interest;determine an intersection of the projected visual attention rays with one or more of the regions of interest; andbased on the intersection, estimate an attention state of the subject's attention.

In some embodiments, the subject attention data includes one or both of eye gaze and head pose data.

In some embodiments, the processor is further configured to determine a confidence measure of visual attention. In some embodiments, the angular distribution of visual attention rays is distributed based, at least in part, on the confidence measure. In some embodiments, the confidence measure is based on the angle of the primary visual attention ray relative to a position and/or orientation of the imaging camera.

