Connaught Electronics Limited

Bayerische Motoren Werke Aktiengesellschaft

Valeo Schalter Und Sensoren Gmbh

Application Solutions (electronics and Vision) Ltd.

Ciaran Hughes

Patrick Bonhoure

Pantelis Ermilios

Guenter Bauer

Stefanie Prinzhausen

Jonathan Horgan

Myles Friel

Lowik Chanussot

Dev Yadav

Leroy Francisco Pereira

Jean Francois Bariant

Matthias Lenk

Mahesh Kondiparthi

Sunil Chandra

Senthil Kumar Yogamani

Jan Thomanek

Duong-Van Nguyen

Chanchal Raj

Robert Voros

Patrick Mcdaid

Petros Kapsalas

Sean Finn

Andreas Westhues

Hadj Hamma Tadjine

Ullrich Scheunert

Mihai Ilie

A method for forming a point cloud corresponding to the topography of an imaged environment involves acquiring an image of the environment with a camera having a WFOV lens mounted on a vehicle; changing the camera pose by an adjustment greater than a threshold; and acquiring another image of the environment with the camera at the changed pose. The images are mapped onto respective surfaces to form respective mapped images, defined by the same nonplanar geometry. One of the mapped images is divided into blocks of pixels. For each block, a depth map is formed by performing a search through the other mapped image to evaluate a disparity in the position of the location of the block of pixels in each of the mapped images. The depth map is converted into a partial point cloud corresponding to the local topography of the environment surrounding the vehicle as it moves through the environment.

Method for measuring the topography of an environment

A method for determining a state of a tailgate () of a pickup vehicle () by a camera system (), includes capturing a cargo bed () of the pickup vehicle () in an image () captured by a camera (), determining a first region of interest (ROI) in the captured image () by an electronic computing device () of the camera system (), wherein the first region of interest (ROI) includes the potential tailgate () at least partially, determining a parameter optically characterizing the tailgate () depending on the captured image (), analyzing the first region of interest (ROI) with regard to the characterizing parameter, and determining a closed state of the tailgate () as state or determining an opened state of the tailgate () as state depending on the analysis of the first region of interest (ROI). A computer program product, an electronic computing device () as well as a camera system () are also disclosed.

Method for determining a state of a tailgate of a pickup vehicle by analysis of a region of interest, computer program product, electronic computing device as well as camera system

A method for determining a camera parameter for a camera () of a camera system () of a pickup vehicle () includes capturing a cargo bed () of the pickup vehicle () in an image () captured by using the camera (), determining at least one first cargo bed edge line of the cargo bed () by optical analysis of the captured image () by using an electronic computing device () of the camera system (), providing a function parameter for a cost function () by analysis of at least one first cargo bed edge line by using a regression-analysis algorithm of a solver module () of the electronic computing device (), and determining the camera parameter on the basis of the cost function () depending on the provided function parameter by using the electronic computing device (). A computer program product, an electronic computing device () as well as a camera system () are also disclosed.

Method for determining a camera parameter for a camera of a pickup vehicle by a regression-analysis algorithm, computer program product, electronic computing device as well as camera system

The invention relates to a method for capturing an object () in an environmental region () of a motor vehicle () based on a sequence of images () of the environmental region (), which are provided by means of a camera () of the motor vehicle (), including the steps of: recognizing a first object feature () in a first image () of the sequence, wherein the first object feature () describes at least a part of the object () in the environmental region (), estimating a position of the object () in the environmental region () based on a predetermined movement model, which describes a movement of the object () in the environmental region (), determining a prediction feature () in a second image () following the first image () in the sequence based on the first object feature () and based on the estimated position, determining a second object feature () in the second image (), associating the second object feature () with the prediction feature () in the second image () if a predetermined association criterion is satisfied, and confirming the second object feature () as originating from the object () if the second object feature () is associated with the prediction feature ().

Method for capturing an object in an environmental region of a motor vehicle with prediction of the movement of the object, camera system as well as motor vehicle

The invention relates to a method for recognizing a raised object on the basis of images in an environmental region of a motor vehicle, comprising: capturing a first image from a first camera and a second image from a second camera, transforming the first and second images into a common reference system, forming gradients over pixel values of pixels along gradient lines in the transformed first and second images, summing gradients along parallel summation lines to form a gradient sum, determining a first pair of the maxima of the gradient sum in the transformed first image and a second pair of the maxima of the gradient sum in the transformed second image, recognizing the raised object, if at least one distance between the maxima of the first pair in the transformed first image deviates from a distance of the maxima of the corresponding second pair in the transformed second image.

Recognizing a raised object on the basis of perspective images

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Tuam, Ireland

Ciaran Hughes