Toyota Motor Europe

Teade Ab

Toyota Motor Europe Nv/sa

Toyota Jidosha Kabushiki Kaisha

Katholieke Universiteit Leuven

Technische Universitat Darmstadt

Cambridge Enterprise Limited

Eth Zurich

Czech Technical University in Prague

Mindvisionlabs Limited

Inria Institut National De Recherche En Informatique Et En Automatique

Institut National De Recherche En Informatique Et En Automatique

Katholieke Universiteit Leuven, K.u. Leuven Research & Development

Gabriel Othmezouri

Katsuhiro Sakai

Roberto Cipolla

Jonas Ambeck-Madsen

Hiromichi Yanagihara

Harald Merkel

Luc Van Gool

Christian Laugier

Ichiro Sakata

Wim Abbeloos

Stamatios Georgoulis

Bernt Schiele

Marc Proesmans

Nilli Lavie

Daniel Olmeda Reino

Luke Palmer

Jiri Matas

Stefan Roth

Konrad Schindler

Christopher Tay Meng Keat

Mykhaylo Andriluka

Nikodem Majer

Wouter Van Gansbeke

Simon Vandenhende

Liqian Ma

Petar Palasek

Ozgur Erkent

Kamel Mekhnacha

Stepán Obdrzálek

Stefan Walk

Vijay Badrinarayanan

Emmanuel Mazer

A control device for a vehicle for determining perceptual load of a visual and dynamic driving scene, the control device being configured to: receive an image sequence representing the driving scene, extract a set of scene features from the image sequence, the set of scene features representing static and/or dynamic information of the driving scene, calculate a time-aggregated representation of the image sequence based on the extracted set of scene features, calculate an attention map of the driving scene by attentional pooling of the time-aggregated representation of the image sequence, and determine the perceptual load of the driving scene based on the attention map. The invention further relates to a corresponding method.

Control device, system and method for determining perceptual load of a visual and dynamic driving scene in real time

A computer-implemented method for training a classifier (Φη), including: training a pretext model (ΦΘ) to learn a pretext task, so as to minimize a distance between an output of a source sample via the pretext model (ΦΘ) and an output of a corresponding transformed sample via the pretext model (ΦΘ), the transformed sample being a sample obtained by applying a transformation (T) to the source sample; S) determining a neighborhood (NXi) of samples (Xi) of a dataset (SD) in the embedding space; S) training the classifier (Φη) to predict respective estimated probabilities Φηj(Xi), j=1 . . . C, for a sample (Xi) to belong to respective clusters (Cj), by using a second training criterion which tends to: maximize a likelihood for a sample and its neighbors (Xj) of its neighborhood (Nxi) to belong to the same cluster; and force the samples to be distributed over several clusters.

Computer-implemented training method, classification method and system and computer-readable recording medium

A system for image completion is disclosed. The system comprises a coordinate generation module configured to receive past frames and a present frame having a first field-of-view and to generate a set of coordinate maps, one for each of the received past frames; and a frame aggregation module configured to receive as input the past frames, the present frame, and the coordinate maps and to synthesize, based on said input, a present frame having a second field-of-view.

System for image completion

The present disclosure relates to a horn antenna or waveguide system comprising a corrugated horn or waveguide, wherein the corrugation takes the form of a helical spiral along the inner surface of the horn or waveguide. The present disclosure further relates to radar antenna.

Helically corrugated horn antenna and helically corrugated waveguide system

The present disclosure relates to a tunable waveguide system comprising a waveguide configured to guide radio waves in at least two dimensions, and an electronically tunable metamaterial configured to tune the radio waves by electronically changing its dielectric and/or conductive characteristics. The present disclosure further relates to a radar antenna system.

Tunable waveguide system

The present disclosure relates to a method of aligning a set of objects, the method comprising the steps of: providing each object with at least one resonant circuit, the resonant circuits having at least one resonance frequency, and aligning the objects until a field coupling between the resonant circuits reaches a predetermined value. The disclosure further relates to an electronic alignment system.

Method of aligning electronic circuits and electronic alignment system

The present disclosure relates to an electronic circuit having a three-dimensional design by comprising a set of two-dimensional electronic circuits, wherein the two-dimensional electronic circuits are coupled with each other by radiation. The disclosure further relates to a radar antenna system.

Three-dimensional electronic circuit

The disclosure relates to a waveguide system comprising a plurality of stacked layers. The system further comprises a waveguide in a direction across the layers by providing each layer with a predetermined metal pattern. The disclosure further relates to a method for forming a waveguide.

Layered waveguide system and method of forming a waveguide

The present disclosure relates to a three-dimensional dielectric structure comprising at least one input and at least one output configured to transmit electromagnetic waves of at least one predetermined wavelength, a metamaterial between the at least one input and the at least one output comprising a substrate and objects with a predetermined dielectric characteristic different to the dielectric characteristic of the substrate, the objects being distributed in the substrate according to a spatially varying distribution function that depends on the wavelength. The disclosure further relates to a method of forming a three-dimensional dielectric structure.

Three-dimensional dielectric structure

An image processor () has a window selector for choosing a detection window within the image, and a self similarity computation part () for determining self-similarity information for a group of the pixels in any part of the detection window, to represent an amount of self-similarity of that group to other groups in any other part of the detector window, and for repeating the determination for groups in all parts of the detection window, to generate a global self similarity descriptor for the detection window. A classifier () is used for classifying whether an object is present based on the global self-similarity descriptor. By using global self-similarity rather than local similarities more information is captured which can lead to better classification. In particular, it helps enable recognition of more distant self-similarities inherent in the object, and self-similarities present at any scale.

Detection of objects in an image using self similarities

A method () and system () is described for processing video data comprising a plurality of images. The method and apparatus is for obtaining for labeling of a plurality of objects or regions in an image of a sequence of images followed by label propagation to other images in the sequence based on an inference step and a model.

Methods and systems for semantic label propagation

Methods and apparatus are described for monocular 3D human pose estimation and tracking, which are able to recover poses of people in realistic street conditions captured using a monocular, potentially moving camera. Embodiments of the present invention provide a three-stage process involving estimating () a 3D pose of each of the multiple objects using an output of 2D tracking-by detection () and 2D viewpoint estimation (). The present invention provides a sound Bayesian formulation to address the above problems. The present invention can provide articulated 3D tracking in realistic street conditions. The present invention provides methods and apparatus for people detection and 2D pose estimation combined with a dynamic motion prior. The present invention provides not only 2D pose estimation for people in side views, it goes beyond this by estimating poses in 3D from multiple viewpoints. The estimation of poses is done in monocular images, and does not require stereo images. Also the present invention does not require detection of characteristic poses of people.

Monocular 3D pose estimation and tracking by detection

Methods and systems for egomotion estimation (e.g. of a vehicle) from visual inputs of a stereo pair of video cameras are described. 3D egomotion estimation is a six degrees of freedom problem in general. In embodiments of the present invention, this is simplified to four dimensions and further decomposed to two two-dimensional sub-solutions. The decomposition allows use of a voting strategy that identifies the most probable solution. An input is a set of image correspondences between two temporally consecutive stereo pairs, i.e. feature points do not need to be tracked over time. The experiments show that even if a trajectory is put together as a simple concatenation of frame-to-frame increments, the results are reliable and precise.

Voting strategy for visual ego-motion from stereo

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Brussels, Belgium

Gabriel Othmezouri