Patents by Inventor Sakthivel Sivaraman
Sakthivel Sivaraman has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Publication number: 20240062067Abstract: Apparatuses, systems, and techniques are described to determine locations of objects using images including digital representations of those objects. In at least one embodiment, a gaze of one or more occupants of a vehicle is determined independently of a location of one or more sensors used to detect those occupants.Type: ApplicationFiled: October 30, 2023Publication date: February 22, 2024Inventors: Feng Hu, Niranjan Avadhanam, Yuzhuo Ren, Sujay Yadawadkar, Sakthivel Sivaraman, Hairong Jiang, Siyue Wu
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Publication number: 20240034260Abstract: 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.Type: ApplicationFiled: October 5, 2023Publication date: February 1, 2024Inventors: Atousa Torabi, Sakthivel Sivaraman, Niranjan Avadhanam, Shagan Sah
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Patent number: 11851014Abstract: 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.Type: GrantFiled: September 7, 2022Date of Patent: December 26, 2023Assignee: NVIDIA CorporationInventors: Atousa Torabi, Sakthivel Sivaraman, Niranjan Avadhanam, Shagan Sah
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Patent number: 11851015Abstract: 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.Type: GrantFiled: September 7, 2022Date of Patent: December 26, 2023Assignee: NVIDIA CorporationInventors: Atousa Torabi, Sakthivel Sivaraman, Niranjan Avadhanam, Shagan Sah
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Patent number: 11803759Abstract: Apparatuses, systems, and techniques are described to determine locations of objects using images including digital representations of those objects. In at least one embodiment, a gaze of one or more occupants of a vehicle is determined independently of a location of one or more sensors used to detect those occupants.Type: GrantFiled: October 11, 2021Date of Patent: October 31, 2023Assignee: Nvidia CorporationInventors: Feng Hu, Niranjan Avadhanam, Yuzhuo Ren, Sujay Yadawadkar, Sakthivel Sivaraman, Hairong Jiang, Siyue Wu
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Publication number: 20230326182Abstract: 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.Type: ApplicationFiled: June 12, 2023Publication date: October 12, 2023Inventors: Sakthivel Sivaraman, Shagan Sah, Niranjan Avadhanam
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Patent number: 11721089Abstract: 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.Type: GrantFiled: January 7, 2022Date of Patent: August 8, 2023Assignee: NVIDIA CorporationInventors: Sakthivel Sivaraman, Shagan Sah, Niranjan Avadhanam
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Patent number: 11688074Abstract: In various examples, a background of an object may be modified to generate a training image. A segmentation mask may be generated and used to generate an object image that includes image data representing the object. The object image may be integrated into a different background and used for data augmentation in training a neural network. Data augmentation may also be performed using hue adjustment (e.g., of the object image) and/or rendering three-dimensional capture data that corresponds to the object from selected views. Inference scores may be analyzed to select a background for an image to be included in a training dataset. Backgrounds may be selected and training images may be added to a training dataset iteratively during training (e.g., between epochs). Additionally, early or late fusion nay be employed that uses object mask data to improve inferencing performed by a neural network trained using object mask data.Type: GrantFiled: September 30, 2020Date of Patent: June 27, 2023Assignee: NVIDIA CorporationInventors: Nishant Puri, Sakthivel Sivaraman, Rajath Shetty, Niranjan Avadhanam
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Publication number: 20230064049Abstract: Interactions with virtual systems may be difficult when users inadvertently fail to provide sufficient information to proceed with their requests. Certain types of inputs, such as auditory inputs, may lack sufficient information to properly provide a response to the user. Additional information, such as image data, may enable user gestures or poses to supplement the auditory inputs to enable response generation without requesting additional information from users.Type: ApplicationFiled: August 31, 2021Publication date: March 2, 2023Inventors: Sakthivel Sivaraman, Nishant Puri, Yuzhuo Ren, Atousa Torabi, Shubhadeep Das, Niranjan Avadhanam, Sumit Kumar Bhattacharya, Jason Roche
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Publication number: 20230001872Abstract: 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.Type: ApplicationFiled: September 7, 2022Publication date: January 5, 2023Inventors: Atousa Torabi, Sakthivel Sivaraman, Niranjan Avadhanam, Shagan Sah
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Publication number: 20220410830Abstract: 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.Type: ApplicationFiled: September 7, 2022Publication date: December 29, 2022Inventors: Atousa Torabi, Sakthivel Sivaraman, Niranjan Avadhanam, Shagan Sah
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Patent number: 11485308Abstract: 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.Type: GrantFiled: June 29, 2020Date of Patent: November 1, 2022Assignee: NVIDIA CorporationInventors: Atousa Torabi, Sakthivel Sivaraman, Niranjan Avadhanam, Shagan Sah
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Publication number: 20220129696Abstract: 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.Type: ApplicationFiled: January 7, 2022Publication date: April 28, 2022Inventors: Sakthivel Sivaraman, Shagan Sah, Niranjan Avadhanam
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Publication number: 20220101047Abstract: In various examples, a background of an object may be modified to generate a training image. A segmentation mask may be generated and used to generate an object image that includes image data representing the object. The object image may be integrated into a different background and used for data augmentation in training a neural network. Data augmentation may also be performed using hue adjustment (e.g., of the object image) and/or rendering three-dimensional capture data that corresponds to the object from selected views. Inference scores may be analyzed to select a background for an image to be included in a training dataset. Backgrounds may be selected and training images may be added to a training dataset iteratively during training (e.g., between epochs). Additionally, early or late fusion nay be employed that uses object mask data to improve inferencing performed by a neural network trained using object mask data.Type: ApplicationFiled: September 30, 2020Publication date: March 31, 2022Inventors: Nishant Puri, Sakthivel Sivaraman, Rajath Shetty, Niranjan Avadhanam
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Publication number: 20220026987Abstract: Apparatuses, systems, and techniques are described to determine locations of objects using images including digital representations of those objects. In at least one embodiment, a gaze of one or more occupants of a vehicle is determined independently of a location of one or more sensors used to detect those occupants.Type: ApplicationFiled: October 11, 2021Publication date: January 27, 2022Inventors: Feng Hu, Niranjan Avadhanam, Yuzhuo Ren, Sujay Yadawadkar, Sakthivel Sivaraman, Hairong Jiang, Siyue Wu
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Patent number: 11222232Abstract: 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.Type: GrantFiled: June 19, 2020Date of Patent: January 11, 2022Assignee: NVIDIA CorporationInventors: Sakthivel Sivaraman, Shagan Sah, Niranjan Avadhanam
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Publication number: 20210402942Abstract: 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.Type: ApplicationFiled: June 29, 2020Publication date: December 30, 2021Inventors: Atousa Torabi, Sakthivel Sivaraman, Niranjan Avadhanam, Shagan Sah
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Publication number: 20210397885Abstract: 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.Type: ApplicationFiled: June 19, 2020Publication date: December 23, 2021Inventors: Sakthivel Sivaraman, Shagan Sah, Niranjan Avadhanam
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Patent number: 11144754Abstract: Apparatuses, systems, and techniques are described to determine locations of objects using images including digital representations of those objects. In at least one embodiment, a gaze of one or more occupants of a vehicle is determined independently of a location of one or more sensors used to detect those occupants.Type: GrantFiled: August 19, 2019Date of Patent: October 12, 2021Assignee: Nvidia CorporationInventors: Feng Hu, Niranjan Avadhanam, Yuzhuo Ren, Sujay Yadawadkar, Sakthivel Sivaraman, Hairong Jiang, Siyue Wu
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Publication number: 20210056306Abstract: Apparatuses, systems, and techniques are described to determine locations of objects using images including digital representations of those objects. In at least one embodiment, a gaze of one or more occupants of a vehicle is determined independently of a location of one or more sensors used to detect those occupants.Type: ApplicationFiled: August 19, 2019Publication date: February 25, 2021Inventors: Feng Hu, Niranjan Avadhanam, Yuzhuo Ren, Sujay Yadawadkar, Sakthivel Sivaraman, Hairong Jiang, Siyue Wu