Patents by Inventor KUNJAN SINGH
KUNJAN SINGH 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: 20230177842Abstract: A segmentation mask can be determined that includes at least one moving object in a plurality of images based on determining eccentricity for each pixel location in the plurality of images. A first image included in the plurality of images can be segmented by applying the segmentation mask to the image. The segmented first image can be transformed to a compressed dense matrix which includes pixel values for non-zero portions of the segmented first image. The compressed dense matrix can be input to a sparse convolutional neural network trained to detect objects. A detected object corresponding to the at least one moving object included in the first image can be output from the sparse convolutional neural network.Type: ApplicationFiled: December 7, 2021Publication date: June 8, 2023Applicant: Ford Global Technologies, LLCInventors: Mostafa Parchami, Xiaomin Li, Enrique Corona, Kunjan Singh
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Publication number: 20230145701Abstract: A camera is positioned to obtain an image of an object. The image is input to a neural network that outputs a three-dimensional (3D) bounding box for the object relative to a pixel coordinate system and object parameters. Then a center of a bottom face of the 3D bounding box is determined in pixel coordinates. The bottom face of the 3D bounding box is located in a ground plane in the image. Based on calibration parameters for the camera that transform pixel coordinates into real-world coordinates, a) a distance from the center of the bottom face of the 3D bounding box to the camera relative to a real-world coordinate system and b) an angle between a line extending from the camera to the center of the bottom face of the 3D bounding box and an optical axis of the camera are determined. The calibration parameters include a camera height relative to the ground plane, a camera focal distance, and a camera tilt relative to the ground plane.Type: ApplicationFiled: September 24, 2021Publication date: May 11, 2023Applicant: Ford Global Technologies, LLCInventors: Mostafa Parchami, Enrique Corona, Kunjan Singh, Gaurav Pandey
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Patent number: 11460851Abstract: A system, comprising a computer that includes a processor and a memory, the memory storing instructions executable by the processor to input a red-green-blue (RGB) image and an eccentricity image to a neural network which outputs a located object based on combining the RGB image and the eccentricity image, wherein the eccentricity image is based on a per-pixel rolling average and a per-pixel rolling variance over a moving window of k video frames. The memory can further include instructions executable by the processor to receive the located object at a computing device included in one or more of a vehicle or a traffic information system.Type: GrantFiled: May 24, 2019Date of Patent: October 4, 2022Assignee: Ford Global Technologies, LLCInventors: Mostafa Parchami, Chandana Neerukonda, Gintaras Vincent Puskorius, Enrique Corona, Kunjan Singh
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Patent number: 11420625Abstract: A computer includes a processor and a memory, the memory storing instructions executable by the processor to collect a plurality of images of one or more targets at an intersection, input the images to a machine learning program to determine a number of the targets to which a host vehicle is predicted to yield at the intersection based on time differences between the plurality of images, and transmit a message indicating the number of the targets.Type: GrantFiled: July 3, 2019Date of Patent: August 23, 2022Assignee: FORD GLOBAL TECHNOLOGIES, LLCInventors: Akhil Perincherry, Kunjan Singh, Nikhil Nagraj Rao
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Patent number: 11403856Abstract: A system, including a processor and a memory, the memory including instructions to be executed by the processor to identify first object features from sensor data acquired by a stationary sensor at a first time step, determine second object features at a second time step. The instructions can include further instructions to determine one or more object clusters of first object features by determining distances measured in pixels between the first object features and corresponding second object features and comparing the distances to one or more mean distances and determine one or more object groups of inlier first object features in the one or more object clusters by determining a plurality of similarity transformations for a plurality of random samples of first object features and determining inlier first object features based on maximizing the number of first object features included in a similarity transformation.Type: GrantFiled: August 26, 2020Date of Patent: August 2, 2022Assignee: FORD GLOBAL TECHNOLOGIES, LLCInventors: Mostafa Parchami, Faizan Shaik, Stephen Giardinelli, Gintaras Vincent Puskorius, Enrique Corona, Kunjan Singh
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Publication number: 20220067407Abstract: A system, including a processor and a memory, the memory including instructions to be executed by the processor to identify first object features from sensor data acquired by a stationary sensor at a first time step, determine second object features at a second time step.Type: ApplicationFiled: August 26, 2020Publication date: March 3, 2022Applicant: Ford Global Technologies, LLCInventors: Mostafa Parchami, Faizan Shaik, Stephen Giardinelli, Gintaras Vincent Puskorius, Enrique Corona, Kunjan Singh
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Publication number: 20210001844Abstract: A computer includes a processor and a memory, the memory storing instructions executable by the processor to collect a plurality of images of one or more targets at an intersection, input the images to a machine learning program to determine a number of the targets to which a host vehicle is predicted to yield at the intersection based on time differences between the plurality of images, and transmit a message indicating the number of the targets.Type: ApplicationFiled: July 3, 2019Publication date: January 7, 2021Applicant: Ford Global Technologies, LLCInventors: Akhil Perincherry, Kunjan Singh, Nikhil Nagraj Rao
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Publication number: 20200371524Abstract: A system, comprising a computer that includes a processor and a memory, the memory storing instructions executable by the processor to input a red-green-blue (RGB) image and an eccentricity image to a neural network which outputs a located object based on combining the RGB image and the eccentricity image, wherein the eccentricity image is based on a per-pixel rolling average and a per-pixel rolling variance over a moving window of k video frames. The memory can further include instructions executable by the processor to receive the located object at a computing device included in one or more of a vehicle or a traffic information system.Type: ApplicationFiled: May 24, 2019Publication date: November 26, 2020Applicant: Ford Global Technologies, LLCInventors: MOSTAFA PARCHAMI, CHANDANA NEERUKONDA, GINTARAS VINCENT PUSKORIUS, ENRIQUE CORONA, KUNJAN SINGH