Toyota Motor Europe Nv/sa

Ucl Business Plc

Toyota Motor Europe

Technische Universitat Darmstadt

Cambridge Enterprise Limited

Czech Technical University in Prague

Camlin Italy S.r.l.

Ichiro Sakata

Gabriel Othmezouri

Jonas Ambeck-Madsen

Gabriel J Brostow

Nilli Lavie

Luke Palmer

Bernt Schiele

Alina Bialkowski

Roberto Cipolla

Luca Ascari

Jiri Matas

Maria Del Vecchio

Giovanni Vecchiato

Pietro Avanzini

Mykhaylo Andriluka

Konrad Schindler

Stefan Roth

Stepán Obdrzálek

Fabio Deon

Stefan Walk

Giacomo Rizzolatti

Nikodem Majer

Vijay Badrinarayanan

The invention relates to a control device () for a vehicle for determining the perceptual load of a visual and dynamic driving scene. The control device is configured to: &#x22c5;receive a sensor output () of a sensor (), the sensor () sensing the visual driving scene, &#x22c5;extract a set of scene features () from the sensor output (), the set of scene features () representing static and/or dynamic information of the visual driving scene, &#x22c5;determine the perceptual load () of the set of extracted scene features () based on a predetermined load model (), the load model () being predetermined based on reference video scenes each being labelled with a load value, &#x22c5;map the perceptual load to the sensed driving and &#x22c5;determine a spatial and temporal intensity distribution of the perceptual load across the sensed driving scene. The invention further relates to a vehicle, a system and a method.

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

A method for determining the intention of a user of a vehicle to brake or accelerate, comprising:

Method and system for determining the intention of a user of a vehicle to brake or accelerate

The invention relates to a control device () for a vehicle for determining the perceptual load of a visual and dynamic driving scene. The control device is configured to: The invention further relates to a system and a method.

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

Ichiro Sakata