Nvidia Corporation

Shagan Sah

Niranjan Avadhanam

Sakthivel Sivaraman

Rajath Shetty

Atousa Torabi

Nishant Puri

Nuri Murat Arar

Pavlo Molchanov

Sujay Yadawadkar

Dieter Fox

Hairong Jiang

Christopher Jason Paxton

Manoj Kumar Yennapureddy

Approaches provide for performance of a complex (e.g., compound) task that may involve multiple discrete tasks not obvious from an instruction to perform the complex task. A set of conditions for an environment can be determined using captured image data, and the instruction analyzed to determine a set of final conditions to exist in the environment after performance of the instruction. These initial and end conditions are used to determine a sequence of discrete tasks to be performed to cause a robot or automated device to perform the instruction. This can involve use of a symbolic or visual planner in at least some embodiments, as well as a search of possible sequences of actions available for the robot or automated device. A robot can be caused to perform the sequence of discrete tasks, and feedback provided such that the sequence of tasks can be modified as appropriate.

Interpreting discrete tasks from complex instructions for robotic systems and applications

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.

Using temporal filters for automated real-time classification

In various examples, techniques are described for detecting whether spoofing attacks are occurring using multiple sensors. Systems and methods are disclosed that include at least a first sensor having a first pose to capture a first perspective view of a user and a second sensor having a second pose to capture a second perspective view of the user. The first sensor and/or the second sensor may include an image sensor, a depth sensor, and/or the like. The systems and methods include a neural network that is configured to analyze first sensor data generated by the first sensor and second sensor data generated by the second sensor to determine whether a spoofing attack is occurring. The systems and methods may also perform one or more processes, such as facial recognition, based on whether the spoofing attack is occurring.

Detecting identity spoofing attacks in multi-sensor systems and applications

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.

In-cabin hazard prevention and safety control system for autonomous machine applications

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.

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.

Neural network based facial analysis using facial landmarks and associated confidence values

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&#x2014;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.

In various examples, an adaptive eye tracking machine learning model engine ('adaptive-model engine') for an eye tracking system is described. The adaptive-model engine may include an eye tracking or gaze tracking development pipeline ('adaptive-model training pipeline') that supports collecting data, training, optimizing, and deploying an adaptive eye tracking model that is a customized eye tracking model based on a set of features of an identified deployment environment. The adaptive-model engine supports ensembling the adaptive eye tracking model that may be trained on gaze vector estimation in surround environments and ensemble based on a plurality of eye tracking variant models and a plurality of facial landmark neural network metrics.

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Santa Clara, CA, United States of America

Shagan Sah