公开(公告)号：US20230326182A1

公开(公告)日：2023-10-12

申请号：US18333281

申请日：2023-06-12

申请人： NVIDIA Corporation

发明人： Sakthivel Sivaraman ,  Shagan Sah ,  Niranjan Avadhanam

IPC分类号： G06V10/764 ,  G06V20/40 ,  G06V40/20 ,  G06V40/10 ,  G06F18/20 ,  G06F18/2321 ,  G06N3/045

CPC分类号： G06V10/764 ,  G06V20/47 ,  G06V40/28 ,  G06V40/113 ,  G06F18/285 ,  G06F18/2321 ,  G06N3/045

摘要： In various examples, the present disclosure relates to using temporal filters for automated real-time classification. The technology described herein improves the performance of a multiclass classifier that may be used to classify a temporal sequence of input signals—such as input signals representative of video frames. A performance improvement may be achieved, at least in part, by applying a temporal filter to an output of the multiclass classifier. For example, the temporal filter may leverage classifications associated with preceding input signals to improve the final classification given to a subsequent signal. In some embodiments, the temporal filter may also use data from a confusion matrix to correct for the probable occurrence of certain types of classification errors. The temporal filter may be a linear filter, a nonlinear filter, an adaptive filter, and/or a statistical filter.

公开(公告)号：US20230078171A1

公开(公告)日：2023-03-16

申请号：US18051296

申请日：2022-10-31

申请人： NVIDIA Corporation

发明人： Nuri Murat Arar ,  Niranjan Avadhanam ,  Nishant Puri ,  Shagan Sah ,  Rajath Shetty ,  Sujay Yadawadkar ,  Pavlo Molchanov

IPC分类号： G06K9/62 ,  G06N20/00 ,  G06V10/94 ,  G06V20/59 ,  G06V20/64 ,  G06V40/16 ,  G06V40/18

摘要： Systems and methods for more accurate and robust determination of subject characteristics from an image of the subject. One or more machine learning models receive as input an image of a subject, and output both facial landmarks and associated confidence values. Confidence values represent the degrees to which portions of the subject's face corresponding to those landmarks are occluded, i.e., the amount of uncertainty in the position of each landmark location. These landmark points and their associated confidence values, and/or associated information, may then be input to another set of one or more machine learning models which may output any facial analysis quantity or quantities, such as the subject's gaze direction, head pose, drowsiness state, cognitive load, or distraction state.

公开(公告)号：US20220410830A1

公开(公告)日：2022-12-29

申请号：US17939613

申请日：2022-09-07

申请人： NVIDIA Corporation

发明人： Atousa Torabi ,  Sakthivel Sivaraman ,  Niranjan Avadhanam ,  Shagan Sah

IPC分类号： B60R21/017 ,  B60R21/013 ,  B60W60/00 ,  G06N3/02 ,  B60W50/14

摘要： In various examples, systems and methods are disclosed that accurately identify driver and passenger in-cabin activities that may indicate a biomechanical distraction that prevents a driver from being fully engaged in driving a vehicle. In particular, image data representative of an image of an occupant of a vehicle may be applied to one or more deep neural networks (DNNs). Using the DNNs, data indicative of key point locations corresponding to the occupant may be computed, a shape and/or a volume corresponding to the occupant may be reconstructed, a position and size of the occupant may be estimated, hand gesture activities may be classified, and/or body postures or poses may be classified. These determinations may be used to determine operations or settings for the vehicle to increase not only the safety of the occupants, but also of surrounding motorists, bicyclists, and pedestrians.

公开(公告)号：US11934955B2

公开(公告)日：2024-03-19

申请号：US18051296

申请日：2022-10-31

申请人： NVIDIA Corporation

发明人： Nuri Murat Arar ,  Niranjan Avadhanam ,  Nishant Puri ,  Shagan Sah ,  Rajath Shetty ,  Sujay Yadawadkar ,  Pavlo Molchanov

IPC分类号： G06N3/08 ,  G06F18/21 ,  G06F18/214 ,  G06N20/00 ,  G06V10/764 ,  G06V10/774 ,  G06V10/82 ,  G06V10/94 ,  G06V20/59 ,  G06V20/64 ,  G06V40/16 ,  G06V40/18

CPC分类号： G06N3/08 ,  G06F18/214 ,  G06F18/2193 ,  G06N20/00 ,  G06V10/764 ,  G06V10/774 ,  G06V10/82 ,  G06V10/95 ,  G06V20/597 ,  G06V20/647 ,  G06V40/171 ,  G06V40/193

摘要： Systems and methods for more accurate and robust determination of subject characteristics from an image of the subject. One or more machine learning models receive as input an image of a subject, and output both facial landmarks and associated confidence values. Confidence values represent the degrees to which portions of the subject's face corresponding to those landmarks are occluded, i.e., the amount of uncertainty in the position of each landmark location. These landmark points and their associated confidence values, and/or associated information, may then be input to another set of one or more machine learning models which may output any facial analysis quantity or quantities, such as the subject's gaze direction, head pose, drowsiness state, cognitive load, or distraction state.

公开(公告)号：US11485308B2

公开(公告)日：2022-11-01

申请号：US16915577

申请日：2020-06-29

申请人： NVIDIA Corporation

发明人： Atousa Torabi ,  Sakthivel Sivaraman ,  Niranjan Avadhanam ,  Shagan Sah

IPC分类号： B60R21/017 ,  B60R21/013 ,  B60W60/00 ,  G06N3/02 ,  B60W50/14 ,  B60W50/00 ,  B60R21/01

摘要： In various examples, systems and methods are disclosed that accurately identify driver and passenger in-cabin activities that may indicate a biomechanical distraction that prevents a driver from being fully engaged in driving a vehicle. In particular, image data representative of an image of an occupant of a vehicle may be applied to one or more deep neural networks (DNNs). Using the DNNs, data indicative of key point locations corresponding to the occupant may be computed, a shape and/or a volume corresponding to the occupant may be reconstructed, a position and size of the occupant may be estimated, hand gesture activities may be classified, and/or body postures or poses may be classified. These determinations may be used to determine operations or settings for the vehicle to increase not only the safety of the occupants, but also of surrounding motorists, bicyclists, and pedestrians.

公开(公告)号：US20210402942A1

公开(公告)日：2021-12-30

申请号：US16915577

申请日：2020-06-29

申请人： NVIDIA Corporation

发明人： Atousa Torabi ,  Sakthivel Sivaraman ,  Niranjan Avadhanam ,  Shagan Sah

IPC分类号： B60R21/017 ,  B60R21/013 ,  B60W60/00 ,  B60W50/14 ,  G06N3/02

摘要： In various examples, systems and methods are disclosed that accurately identify driver and passenger in-cabin activities that may indicate a biomechanical distraction that prevents a driver from being fully engaged in driving a vehicle. In particular, image data representative of an image of an occupant of a vehicle may be applied to one or more deep neural networks (DNNs). Using the DNNs, data indicative of key point locations corresponding to the occupant may be computed, a shape and/or a volume corresponding to the occupant may be reconstructed, a position and size of the occupant may be estimated, hand gesture activities may be classified, and/or body postures or poses may be classified. These determinations may be used to determine operations or settings for the vehicle to increase not only the safety of the occupants, but also of surrounding motorists, bicyclists, and pedestrians.

公开(公告)号：US11851015B2

公开(公告)日：2023-12-26

申请号：US17939622

申请日：2022-09-07

申请人： NVIDIA Corporation

发明人： Atousa Torabi ,  Sakthivel Sivaraman ,  Niranjan Avadhanam ,  Shagan Sah

IPC分类号： B60R21/017 ,  B60R21/013 ,  B60W60/00 ,  G06N3/02 ,  B60W50/14 ,  B60W50/00 ,  B60R21/01

CPC分类号： B60R21/017 ,  B60R21/013 ,  B60W50/14 ,  B60W60/005 ,  G06N3/02 ,  B60R2021/01211 ,  B60R2021/01286 ,  B60W2050/0062

摘要： In various examples, systems and methods are disclosed that accurately identify driver and passenger in-cabin activities that may indicate a biomechanical distraction that prevents a driver from being fully engaged in driving a vehicle. In particular, image data representative of an image of an occupant of a vehicle may be applied to one or more deep neural networks (DNNs). Using the DNNs, data indicative of key point locations corresponding to the occupant may be computed, a shape and/or a volume corresponding to the occupant may be reconstructed, a position and size of the occupant may be estimated, hand gesture activities may be classified, and/or body postures or poses may be classified. These determinations may be used to determine operations or settings for the vehicle to increase not only the safety of the occupants, but also of surrounding motorists, bicyclists, and pedestrians.

公开(公告)号：US20230001872A1

公开(公告)日：2023-01-05

申请号：US17939622

申请日：2022-09-07

申请人： NVIDIA Corporation

发明人： Atousa Torabi ,  Sakthivel Sivaraman ,  Niranjan Avadhanam ,  Shagan Sah

IPC分类号： B60R21/017 ,  B60R21/013 ,  B60W60/00 ,  G06N3/02 ,  B60W50/14

摘要： In various examples, systems and methods are disclosed that accurately identify driver and passenger in-cabin activities that may indicate a biomechanical distraction that prevents a driver from being fully engaged in driving a vehicle. In particular, image data representative of an image of an occupant of a vehicle may be applied to one or more deep neural networks (DNNs). Using the DNNs, data indicative of key point locations corresponding to the occupant may be computed, a shape and/or a volume corresponding to the occupant may be reconstructed, a position and size of the occupant may be estimated, hand gesture activities may be classified, and/or body postures or poses may be classified. These determinations may be used to determine operations or settings for the vehicle to increase not only the safety of the occupants, but also of surrounding motorists, bicyclists, and pedestrians.

公开(公告)号：US20220129696A1

公开(公告)日：2022-04-28

申请号：US17647390

申请日：2022-01-07

申请人： NVIDIA Corporation

发明人： Sakthivel Sivaraman ,  Shagan Sah ,  Niranjan Avadhanam

IPC分类号： G06K9/62 ,  G06N3/04 ,  G06V10/32 ,  G06V20/40 ,  G06V40/20 ,  G06V40/10

摘要： In various examples, the present disclosure relates to using temporal filters for automated real-time classification. The technology described herein improves the performance of a multiclass classifier that may be used to classify a temporal sequence of input signals—such as input signals representative of video frames. A performance improvement may be achieved, at least in part, by applying a temporal filter to an output of the multiclass classifier. For example, the temporal filter may leverage classifications associated with preceding input signals to improve the final classification given to a subsequent signal. In some embodiments, the temporal filter may also use data from a confusion matrix to correct for the probable occurrence of certain types of classification errors. The temporal filter may be a linear filter, a nonlinear filter, an adaptive filter, and/or a statistical filter.

公开(公告)号：US12073604B2

公开(公告)日：2024-08-27

申请号：US18333281

申请日：2023-06-12

申请人： NVIDIA Corporation

发明人： Sakthivel Sivaraman ,  Shagan Sah ,  Niranjan Avadhanam

IPC分类号： G06V10/00 ,  G06F18/20 ,  G06F18/2321 ,  G06N3/045 ,  G06V10/764 ,  G06V20/40 ,  G06V40/10 ,  G06V40/20

CPC分类号： G06V10/764 ,  G06F18/2321 ,  G06F18/285 ,  G06N3/045 ,  G06V20/47 ,  G06V40/113 ,  G06V40/28

摘要： In various examples, the present disclosure relates to using temporal filters for automated real-time classification. The technology described herein improves the performance of a multiclass classifier that may be used to classify a temporal sequence of input signals—such as input signals representative of video frames. A performance improvement may be achieved, at least in part, by applying a temporal filter to an output of the multiclass classifier. For example, the temporal filter may leverage classifications associated with preceding input signals to improve the final classification given to a subsequent signal. In some embodiments, the temporal filter may also use data from a confusion matrix to correct for the probable occurrence of certain types of classification errors. The temporal filter may be a linear filter, a nonlinear filter, an adaptive filter, and/or a statistical filter.