公开(公告)号：US10887614B2

公开(公告)日：2021-01-05

申请号：US16450743

申请日：2019-06-24

Applicant: Intel Corporation

Inventor： Srenivas Varadarajan ,  Omesh Tickoo ,  Vallabhajosyula Somayazulu ,  Yiting Liao ,  Ibrahima Ndiour ,  Shao-Wen Yang ,  Yen-Kuang Chen

IPC: H04N19/42 ,  H04N19/176 ,  H04N19/167 ,  H04N19/70 ,  H04N19/513 ,  H04N19/119 ,  H04N19/115 ,  H04N19/137 ,  H04N19/17

Abstract: Techniques related to applying computer vision to decompressed video are discussed. Such techniques may include generating a region of interest in an individual video frame by translating spatial indicators of a first detected computer vision result from a reference video frame to the individual video frame and applying a greater threshold within the region of interest than outside of the region of interest for computer vision evaluation in the individual frame.

公开(公告)号：US10740617B2

公开(公告)日：2020-08-11

申请号：US15846466

申请日：2017-12-19

Applicant: Intel Corporation

Inventor： Srenivas Varadarajan ,  Omesh Tickoo

IPC: G06K9/00 ,  H04N7/18

Abstract: A mechanism is described for facilitating protection and recovery of identities in surveillance camera environments according to one embodiment. An apparatus of embodiments, as described herein, includes detection and reception logic to receive a video stream of a scene as captured by a camera, wherein the scene includes persons. The apparatus may further include recognition and application logic to recognize an abnormal activity and one or more persons associated with the abnormal activity in a video frame of the video stream. The apparatus may further include identity recovery logic to recover one or more identities of the one or more persons in response to the abnormal activity, where the one or more identities are recovered from masked data and encrypted residuals associated with the one or more persons.

公开(公告)号：US20200160049A1

公开(公告)日：2020-05-21

申请号：US16748212

申请日：2020-01-21

Applicant: Intel Corporation

Inventor： Srenivas Varadarajan ,  Nikita Tiwari ,  Parual Datta ,  Andradige Pubudu Madhawa Silva ,  Omesh Tickoo ,  Erin Carroll

IPC: G06K9/00 ,  G06T7/292

Abstract: A mechanism is described for facilitating age classification of humans using image depth and human pose according to one embodiment. A method of embodiments, as described herein, includes facilitating, by one or more cameras of a computing device, capturing of a video stream of a scene having persons, and computing overall-depth torso lengths of the persons based on depth torso lengths of the persons. The method may further include comparing the overall-depth torso lengths with a predetermined threshold value representing a separation age between adults and children, and classifying a first set of the persons as adults if a first set of the overall-depth torso lengths associated with the first set of persons is greater than the threshold value.

公开(公告)号：US10540545B2

公开(公告)日：2020-01-21

申请号：US15821461

申请日：2017-11-22

Applicant: Intel Corporation

Inventor： Srenivas Varadarajan ,  Nikita Tiwari ,  Parual Datta ,  Andradige Pubudu Madhawa Silva ,  Omesh Tickoo ,  Erin Carroll

IPC: G06K9/00 ,  G06T7/292 ,  G06K9/62

Abstract: A mechanism is described for facilitating age classification of humans using image depth and human pose according to one embodiment. A method of embodiments, as described herein, includes facilitating, by one or more cameras of a computing device, capturing of a video stream of a scene having persons, and computing overall-depth torso lengths of the persons based on depth torso lengths of the persons. The method may further include comparing the overall-depth torso lengths with a predetermined threshold value representing a separation age between adults and children, and classifying a first set of the persons as adults if a first set of the overall-depth torso lengths associated with the first set of persons is greater than the threshold value.

公开(公告)号：US10349060B2

公开(公告)日：2019-07-09

申请号：US15640202

申请日：2017-06-30

Applicant: INTEL CORPORATION

Inventor： Srenivas Varadarajan ,  Yiting Liao ,  Vallabhajosyula S. Somayazulu ,  Omesh Tickoo ,  Ibrahima J. Ndiour ,  Javier Perez-Ramirez

IPC: H04N19/167 ,  H04N19/17 ,  H04N19/176

Abstract: An example apparatus for encoding video frames includes a receiver to receive video frames and a heat map from a camera and expected object regions from a video database. The apparatus also includes a region of interest (ROI) map generator to detect a region of interest in a video frame based on the expected object regions. The ROI map generator can also detect a region of interest in the video frame based on the heat map. The ROI map generator can then generate an ROI map based on the detected regions of interest. The apparatus further includes a parameter adjuster to adjust an encoding parameter based on the ROI map. The apparatus also further includes a video encoder to encode the video frame using the adjusted encoding parameter.

公开(公告)号：US10198818B2

公开(公告)日：2019-02-05

申请号：US15291507

申请日：2016-10-12

Applicant: INTEL CORPORATION

Inventor： Srenivas Varadarajan ,  Praveen Gopalakrishnan ,  Victor B. Lortz

IPC: G06K9/00 ,  G06T7/00

Abstract: In one example, a system for recognizing an object includes a processor to select from a plurality of image frames an image frame in which a view of the object is not blocked, and to estimate a location of the object in the selected image frame.

公开(公告)号：US11836240B2

公开(公告)日：2023-12-05

申请号：US18157154

申请日：2023-01-20

Applicant: Intel Corporation

Inventor： Yen-Kuang Chen ,  Shao-Wen Yang ,  Ibrahima J. Ndiour ,  Yiting Liao ,  Vallabhajosyula S. Somayazulu ,  Omesh Tickoo ,  Srenivas Varadarajan

IPC: G06F21/44 ,  H04L9/06 ,  G06F21/64 ,  G06F21/53 ,  G06N5/022 ,  G06F21/45 ,  H04L9/32 ,  H04W4/70 ,  G06F16/538 ,  G06F16/535 ,  G06F16/54 ,  G06F21/62 ,  G06F9/50 ,  G06N3/04 ,  G06N3/063 ,  G06V10/44 ,  G06V20/00 ,  G06V40/20 ,  G06V40/16 ,  G06F9/48 ,  H04L67/51 ,  G06T7/11 ,  G06V10/96 ,  G06V30/262 ,  G06K15/02 ,  G06F18/24 ,  G06F18/21 ,  G06F18/22 ,  G06F18/211 ,  G06F18/213 ,  G06F18/2413 ,  G06N3/045 ,  G06V30/19 ,  G06V10/82 ,  G06V10/94 ,  G06V10/75 ,  G06V10/20 ,  G06V10/40 ,  G06N3/08 ,  H04L67/12 ,  H04N19/80 ,  G06F16/951 ,  H04N19/46 ,  G06T7/70 ,  H04W12/02 ,  H04L9/00 ,  H04N19/12 ,  H04N19/124 ,  H04N19/167 ,  H04N19/172 ,  H04N19/176 ,  H04N19/44 ,  H04N19/48 ,  H04N19/513 ,  G06T7/20 ,  G06F18/243 ,  G06V30/194 ,  H04N19/42 ,  H04N19/625 ,  H04N19/63 ,  G06T7/223 ,  H04L67/10

CPC classification number: G06F21/44 ,  G06F9/4881 ,  G06F9/5044 ,  G06F9/5066 ,  G06F9/5072 ,  G06F16/535 ,  G06F16/538 ,  G06F16/54 ,  G06F16/951 ,  G06F18/21 ,  G06F18/211 ,  G06F18/213 ,  G06F18/2163 ,  G06F18/22 ,  G06F18/24 ,  G06F18/24143 ,  G06F21/45 ,  G06F21/53 ,  G06F21/6254 ,  G06F21/64 ,  G06K15/1886 ,  G06N3/04 ,  G06N3/045 ,  G06N3/063 ,  G06N3/08 ,  G06N5/022 ,  G06T7/11 ,  G06T7/70 ,  G06V10/20 ,  G06V10/40 ,  G06V10/454 ,  G06V10/75 ,  G06V10/82 ,  G06V10/95 ,  G06V10/96 ,  G06V20/00 ,  G06V30/19173 ,  G06V30/274 ,  G06V40/161 ,  G06V40/20 ,  H04L9/0643 ,  H04L9/3239 ,  H04L67/12 ,  H04L67/51 ,  H04N19/46 ,  H04N19/80 ,  H04W4/70 ,  G06F18/24323 ,  G06F2209/503 ,  G06F2209/506 ,  G06F2221/2117 ,  G06T7/20 ,  G06T7/223 ,  G06T2207/10016 ,  G06T2207/20021 ,  G06T2207/20024 ,  G06T2207/20052 ,  G06T2207/20056 ,  G06T2207/20064 ,  G06T2207/20084 ,  G06T2207/20221 ,  G06T2207/30242 ,  G06V30/194 ,  G06V2201/10 ,  H04L9/50 ,  H04L67/10 ,  H04N19/12 ,  H04N19/124 ,  H04N19/167 ,  H04N19/172 ,  H04N19/176 ,  H04N19/42 ,  H04N19/44 ,  H04N19/48 ,  H04N19/513 ,  H04N19/625 ,  H04N19/63 ,  H04W12/02

Abstract: In one embodiment, an apparatus comprises a memory and a processor. The memory is to store visual data associated with a visual representation captured by one or more sensors. The processor is to: obtain the visual data associated with the visual representation captured by the one or more sensors, wherein the visual data comprises uncompressed visual data or compressed visual data; process the visual data using a convolutional neural network (CNN), wherein the CNN comprises a plurality of layers, wherein the plurality of layers comprises a plurality of filters, and wherein the plurality of filters comprises one or more pixel-domain filters to perform processing associated with uncompressed data and one or more compressed-domain filters to perform processing associated with compressed data; and classify the visual data based on an output of the CNN.

公开(公告)号：US11531850B2

公开(公告)日：2022-12-20

申请号：US16947590

申请日：2020-08-07

Applicant: Intel Corporation

Inventor： Yen-Kuang Chen ,  Shao-Wen Yang ,  Ibrahima J. Ndiour ,  Yiting Liao ,  Vallabhajosyula S. Somayazulu ,  Omesh Tickoo ,  Srenivas Varadarajan

IPC: G06K9/62 ,  G06F9/48 ,  G06F9/50 ,  G06F16/535 ,  G06F16/538 ,  G06F16/951 ,  G06F21/44 ,  G06F21/45 ,  G06F21/53 ,  G06F21/62 ,  G06F21/64 ,  H04L9/06 ,  G06N5/02 ,  G06N3/04 ,  H04L9/32 ,  H04W4/70 ,  G06F16/54 ,  G06N3/063 ,  G06V10/20 ,  G06V10/40 ,  G06V10/75 ,  G06V10/44 ,  G06V20/00 ,  G06V40/20 ,  G06V40/16 ,  H04L67/51 ,  G06T7/11 ,  G06V10/96 ,  G06V30/262 ,  G06K15/02 ,  G06N3/08 ,  H04L67/12 ,  H04N19/80 ,  H04N19/46 ,  G06T7/70 ,  H04W12/02 ,  H04L9/00 ,  H04N19/12 ,  H04N19/124 ,  H04N19/167 ,  H04N19/172 ,  H04N19/176 ,  H04N19/44 ,  H04N19/48 ,  H04N19/513 ,  G06V30/194 ,  G06T7/20 ,  H04N19/42 ,  H04N19/625 ,  H04N19/63 ,  G06T7/223 ,  H04L67/10

Abstract: In one embodiment, an apparatus comprises a storage device and a processor. The storage device may store a plurality of compressed images comprising one or more compressed master images and one or more compressed slave images. The processor may: identify an uncompressed image; access context information associated with the uncompressed image and the one or more compressed master images; determine, based on the context information, whether the uncompressed image is associated with a corresponding master image; upon a determination that the uncompressed image is associated with the corresponding master image, compress the uncompressed image into a corresponding compressed image with reference to the corresponding master image; upon a determination that the uncompressed image is not associated with the corresponding master image, compress the uncompressed image into the corresponding compressed image without reference to the one or more compressed master images; and store the corresponding compressed image on the storage device.

公开(公告)号：US20220191537A1

公开(公告)日：2022-06-16

申请号：US17509246

申请日：2021-10-25

Applicant: Intel Corporation

Inventor： Yiting Liao ,  Yen-Kuang Chen ,  Shao-Wen Yang ,  Vallabhajosyula S. Somayazulu ,  Srenivas Varadarajan ,  Omesh Tickoo ,  Ibrahima J. Ndiour

IPC: H04N19/52 ,  H04N19/523 ,  G06N3/04 ,  G06K9/62 ,  H04N19/172 ,  G06V10/20

Abstract: In one embodiment, an apparatus comprises processing circuitry to: receive, via a communication interface, a compressed video stream captured by a camera, wherein the compressed video stream comprises: a first compressed frame; and a second compressed frame, wherein the second compressed frame is compressed based at least in part on the first compressed frame, and wherein the second compressed frame comprises a plurality of motion vectors; decompress the first compressed frame into a first decompressed frame; perform pixel-domain object detection to detect an object at a first position in the first decompressed frame; and perform compressed-domain object detection to detect the object at a second position in the second compressed frame, wherein the object is detected at the second position in the second compressed frame based on: the first position of the object in the first decompressed frame; and the plurality of motion vectors from the second compressed frame.

公开(公告)号：US20210243012A1

公开(公告)日：2021-08-05

申请号：US16948304

申请日：2020-09-11

Applicant: Intel Corporation

Inventor： Yen-Kuang Chen ,  Shao-Wen Yang ,  Ibrahima J. Ndiour ,  Yiting Liao ,  Vallabhajosyula S. Somayazulu ,  Omesh Tickoo ,  Srenivas Varadarajan

IPC: H04L9/06 ,  G06F21/64 ,  G06F21/53 ,  G06N5/02 ,  G06K9/00 ,  G06N3/04 ,  H04L29/08 ,  G06F21/45 ,  H04L9/32 ,  H04W4/70 ,  G06F21/44 ,  G06K9/46 ,  G06K9/62 ,  G06F16/538 ,  G06F16/535 ,  G06F16/54 ,  G06F21/62 ,  G06F9/50 ,  G06N3/063 ,  G06N3/08 ,  H04N19/80 ,  G06F16/951 ,  G06K9/36 ,  H04N19/46 ,  G06T7/70 ,  G06K9/64 ,  G06K9/72

Abstract: In one embodiment, an apparatus comprises a memory and a processor. The memory is to store visual data associated with a visual representation captured by one or more sensors. The processor is to: obtain the visual data associated with the visual representation captured by the one or more sensors, wherein the visual data comprises uncompressed visual data or compressed visual data; process the visual data using a convolutional neural network (CNN), wherein the CNN comprises a plurality of layers, wherein the plurality of layers comprises a plurality of filters, and wherein the plurality of filters comprises one or more pixel-domain filters to perform processing associated with uncompressed data and one or more compressed-domain filters to perform processing associated with compressed data; and classify the visual data based on an output of the CNN.