Intel Corporation

Hyundai Motor Company

Kia Corporation

Ibrahima J Ndiour

Omesh Tickoo

Vallabhajosyula S Somayazulu

Yiting Liao

Srenivas Varadarajan

Shao-Wen Yang

Yen-kuang Chen

Nilesh Amar Ahuja

Ranganath Krishnan

Javier Felip Leon

Javier Perez-Ramirez

David Gomez Gutierrez

Mahesh M Subedar

Ignacio Javier Alvarez

Jorge E Caviedes

Nilesh Ahuja

In one embodiment, an apparatus comprises a communication interface and a processor. The communication interface is to communicate with a plurality of devices. The processor is to: receive compressed data from a first device, wherein the compressed data is associated with visual data captured by sensor(s); perform a current stage of processing on the compressed data using a current CNN, wherein the current stage of processing corresponds to one of a plurality of processing stages associated with the visual data, and wherein the current CNN corresponds to one of a plurality of CNNs associated with the plurality of processing stages; obtain an output associated with the current stage of processing; determine, based on the output, whether processing associated with the visual data is complete; if the processing is complete, output a result associated with the visual data; if the processing is incomplete, transmit the compressed data to a second device.

Compressed video recognition

Techniques are disclosed for using neural network architectures to estimate predictive uncertainty measures, which quantify how much trust should be placed in the deep neural network (DNN) results. The techniques include measuring reliable uncertainty scores for a neural network, which are widely used in perception and decision-making tasks in automated driving. The uncertainty measurements are made with respect to both model uncertainty and data uncertainty, and may implement Bayesian neural networks or other types of neural networks.

Robust multimodal sensor fusion for autonomous driving vehicles

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.

Frequency-domain convolutional neural network

Vehicle navigation control systems in autonomous driving rely on accurate predictions of objects within the vicinity of the vehicle to appropriately control the vehicle safely through its surrounding environment. Accordingly this disclosure provides methods and devices which implement mechanisms for obtaining contextual variables of the vehicle's environment for use in determining the accuracy of predictions of objects within the vehicle's environment.

Devices and methods for accurately identifying objects in a vehicle's environment

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.

Hybrid pixel-domain and compressed-domain video analytics framework

Multi-domain convolutional neural network

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.

Context-aware image compression

Cascade convolutional neural network

A safety system for a vehicle may include one or more processors configured to determine uncertainty data indicating uncertainty in one or more predictions from a driving model during operation of a vehicle; change or update one or more of the driving model parameters to one or more changed or updated driving model parameters based on the determined uncertainty data; and provide the one or more changed or updated driving model parameters to a control system of the vehicle for controlling the vehicle to operate in accordance with the driving model including the one or more changed or updated driving model parameters.

Safety system for a vehicle

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.

Adaptive thresholding for computer vision on low bitrate compressed video streams

Multi-domain cascade convolutional neural network

An example apparatus for encoding video frames includes a receiver to receive events from a dynamic vision sensor and a video frame from an image sensor. The apparatus also includes a heat map generator to generate a heat map based on the received events. The apparatus further includes a region of interest (ROI) map generator generate a ROI map based on the heat map. The apparatus 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 parameter.

Growing community of inventors

Portland, OR, United States of America

Ibrahima J Ndiour