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公开(公告)号：US10977502B2
公开(公告)日：2021-04-13
申请号：US16272415
申请日：2019-02-11
Applicant: Texas Instruments Incorporated
Inventor： Prashanth Ramanathpur Viswanath , Deepak Kumar Poddar , Soyeb Nagori , Manu Mathew
Abstract: A method for estimating time to collision (TTC) of a detected object in a computer vision system is provided that includes determining a three dimensional (3D) position of a camera in the computer vision system, determining a 3D position of the detected object based on a 2D position of the detected object in an image captured by the camera and an estimated ground plane corresponding to the image, computing a relative 3D position of the camera, a velocity of the relative 3D position, and an acceleration of the relative 3D position based on the 3D position of the camera and the 3D position of the detected object, wherein the relative 3D position of the camera is relative to the 3D position of the detected object, and computing the TTC of the detected object based on the relative 3D position, the velocity, and the acceleration.
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公开(公告)号：US10890445B2
公开(公告)日：2021-01-12
申请号：US16185256
申请日：2018-11-09
Applicant: Texas Instruments Incorporated
Inventor： Soyeb Nagori , Poorna Kumar , Manu Mathew , Prashanth Ramanathpur Viswanath , Deepak Kumar Poddar
IPC: G01C3/08 , G06T7/73 , G06T7/77 , G06T7/579 , B60R11/04 , G01C5/00 , G01C21/26 , G06K9/00 , G06K9/62
Abstract: Estimation of the ground plane of a three dimensional (3D) point cloud based modifications to the random sample consensus (RANSAC) algorithm is provided. The modifications may include applying roll and pitch constraints to the selection of random planes in the 3D point cloud, using a cost function based on the number of inliers in the random plane and the number of 3D points below the random plane in the 3D point cloud, and computing a distance threshold for the 3D point cloud that is used in determining whether or not a 3D point in the 3D point cloud is an inlier of a random plane.
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公开(公告)号：US10824934B2
公开(公告)日：2020-11-03
申请号：US15784588
申请日：2017-10-16
Applicant: Texas Instruments Incorporated
Inventor： Mihir Narendra Mody , Shyam Jagannathan , Manu Mathew , Jason T. Jones
Abstract: Described examples include an integrated circuit including a vector multiply unit including a plurality of multiply/accumulate nodes, in which the vector multiply unit is operable to provide an output from the multiply/accumulate nodes, a first data feeder operable to provide first data to the vector multiply unit in vector format, and a second data feeder operable to provide second data to the vector multiply unit in vector format.
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公开(公告)号：US10657395B2
公开(公告)日：2020-05-19
申请号：US16376438
申请日：2019-04-05
Applicant: TEXAS INSTRUMENTS INCORPORATED
Inventor： Arun Shankar Kudana , Manu Mathew , Soyeb Nagori
Abstract: Advanced driver assistance systems can be designed to recognize and to classify traffic signs under real time constraints, and under a wide variety of visual conditions. This disclosure provides techniques that employ binary masks extracted by color space segmentation, with a different binary mask generated for each sign shape. Temporal tracking is employed to add robustness to the detection system. The system is generic, and is trainable to the traffic signs used in various countries.
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公开(公告)号：US20190236383A1
公开(公告)日：2019-08-01
申请号：US16376438
申请日：2019-04-05
Applicant: TEXAS INSTRUMENTS INCORPORATED
Inventor： Arun Shankar Kudana , Manu Mathew , Soyeb Nagori
CPC classification number: G06K9/00818 , G06K9/4633 , G06K9/6257
Abstract: Advanced driver assistance systems can be designed to recognize and to classify traffic signs under real time constraints, and under a wide variety of visual conditions. This disclosure provides techniques that employ binary masks extracted by color space segmentation, with a different binary mask generated for each sign shape. Temporal tracking is employed to add robustness to the detection system. The system is generic, and is trainable to the traffic signs used in various countries.
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Patent Agency Ranking
公开(公告)号：US20190124326A1
公开(公告)日：2019-04-25
申请号：US16228350
申请日：2018-12-20
Applicant: TEXAS INSTRUMENTS INCORPORATED
Inventor： Soyeb Nagori , Manu Mathew , Pramod Kumar Swami
IPC: H04N19/107 , H04N19/50 , H04N19/176 , H04N19/147
CPC classification number: H04N19/107 , H04N19/147 , H04N19/176 , H04N19/50
Abstract: This invention predicts that intra mode prediction is more effective for the macroblocks where motion estimation in inter mode prediction fails. This failure is indicated by a large value of the inter mode SAD. This invention performs intra mode prediction for only macro blocks have larger inter mode SADs. The definition of a large inter mode SAD differs for different content. This invention compares the inter mode SAD of a current macroblock with an adaptive threshold. This adaptive threshold depends on the average and variance of the SADs of the previous predicted frame. An adaptive threshold is calculated for each new predictive frame.
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公开(公告)号：US10248872B2
公开(公告)日：2019-04-02
申请号：US15298218
申请日：2016-10-19
Applicant: Texas Instruments Incorporated
Inventor： Prashanth Ramanathpur Viswanath , Deepak Kumar Poddar , Soyed Nagori , Manu Mathew
Abstract: A method for estimating time to collision (TTC) of a detected object in a computer vision system is provided that includes determining a three dimensional (3D) position of a camera in the computer vision system, determining a 3D position of the detected object based on a 2D position of the detected object in an image captured by the camera and an estimated ground plane corresponding to the image, computing a relative 3D position of the camera, a velocity of the relative 3D position, and an acceleration of the relative 3D position based on the 3D position of the camera and the 3D position of the detected object, wherein the relative 3D position of the camera is relative to the 3D position of the detected object, and computing the TTC of the detected object based on the relative 3D position, the velocity, and the acceleration.
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公开(公告)号：US10083374B2
公开(公告)日：2018-09-25
申请号：US15376473
申请日：2016-12-12
Applicant: Texas Instruments Incorporated
Inventor： Mihir Narendra Mody , Manu Mathew , Chaitanya Satish Ghone
CPC classification number: G06K9/4628 , G06K9/522 , G06K9/6271
Abstract: A method for analyzing images to generate a plurality of output features includes receiving input features of the image and performing Fourier transforms on each input feature. Kernels having coefficients of a plurality of trained features are received and on-the-fly Fourier transforms (OTF-FTs) are performed on the coefficients in the kernels. The output of each Fourier transform and each OTF-FT are multiplied together to generate a plurality of products and each of the products are added to produce one sum for each output feature. Two-dimensional inverse Fourier transforms are performed on each sum.
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公开(公告)号：US12184840B2
公开(公告)日：2024-12-31
申请号：US17875305
申请日：2022-07-27
Applicant: TEXAS INSTRUMENTS INCORPORATED
Inventor： Soyeb Nagori , Manu Mathew , Pramod Kumar Swami
IPC: H04N19/107 , H04N19/147 , H04N19/176 , H04N19/50
Abstract: This invention predicts that intra mode prediction is more effective for the macroblocks where motion estimation in inter mode prediction fails. This failure is indicated by a large value of the inter mode SAD. This invention performs intra mode prediction for only macro blocks have larger inter mode SADs. The definition of a large inter mode SAD differs for different content. This invention compares the inter mode SAD of a current macroblock with an adaptive threshold. This adaptive threshold depends on the average and variance of the SADs of the previous predicted frame. An adaptive threshold is calculated for each new predictive frame.
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公开(公告)号：US11915117B2
公开(公告)日：2024-02-27
申请号：US17327988
申请日：2021-05-24
Applicant: TEXAS INSTRUMENTS INCORPORATED
Inventor： Manu Mathew , Kumar Desappan , Pramod Kumar Swami
CPC classification number: G06N3/04 , G06F7/5443 , G06F17/15 , G06F17/153 , G06N3/045 , G06N3/08 , G06N3/082 , G06N3/084 , G06N3/10
Abstract: A method for convolution in a convolutional neural network (CNN) is provided that includes accessing a coefficient value of a filter corresponding to an input feature map of a convolution layer of the CNN, and performing a block multiply accumulation operation on a block of data elements of the input feature map, the block of data elements corresponding to the coefficient value, wherein, for each data element of the block of data elements, a value of the data element is multiplied by the coefficient value and a result of the multiply is added to a corresponding data element in a corresponding output block of data elements comprised in an output feature map.
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