Abstract: A method and device for determining an ego-motion of a vehicle are disclosed. Respective sequences of consecutive images are obtained from a front view camera, a left side view camera, a right side view camera and a rear view camera and merged. A virtual projection of the images to a ground plane is provided using an affine projection. An optical flow is determined from the sequence of projected images, an ego-motion of the vehicle is determined from the optical flow and the ego-motion is used to predict a kinematic state of the car.

Abstract: The application provides a method of calibrating a camera of a vehicle. The vehicle has a reference frame. The method comprises taking an image of a scene by the camera. The ground plane of the vehicle is then determined according to features of the image. An origin point of the vehicle reference frame is later defined as being located on the determined ground plane. A translation of a reference frame of the camera is afterward determined for aligning the camera reference frame with the vehicle reference frame.

Abstract: A method and a device for image stabilization of an image sequence of one or more cameras of a vehicle are disclosed. Images of the surroundings of the vehicle are recorded and image points of the recorded images are projected to a unit sphere, where the projection includes applying a lens distortion correction. For each of the one or more cameras, a vanishing point is calculated using the projected image points on the unit sphere and a motion of the vanishing point on the unit sphere is tracked. The motion of the vanishing point is used to calculate a corrected projection to a ground plane.

Abstract: An enhanced object detecting method and apparatus is presented. A plurality of successive frames is captured by a monocular camera and the image data of the captured frames are transformed with respect to a predetermined point of view. For instance, the images may be transformed in order to obtain a top-down view. Particular features such as lines are extracted from the transformed image data, and corresponding features of successive frames are matched. An angular change of corresponding features is determined and boundaries of an object are identified based on the angular change of the features.

Abstract: This disclosure relates to a method in which points common to two or more of the images that appear to represent the same real world features are identified, and changes in the location of the points between respective images are used to deduce the motion of the camera and to find the position of the real world features in three dimensional space. In order to determine the scale of the three dimensional information, the position of a reference feature, whose actual distance from the camera is known, is found from the images. The reference feature is found by considering only candidate points selected from candidate points falling within a portion of the image corresponding to a part of the field of view of the camera. The scale is determined from the distance between the camera and the reference feature in the image and in real life.

Abstract: A method and apparatus for calibrating an image from a camera mounted on a vehicle using camera pose parameter determined from information relating to suspension level from a suspension system of the vehicle. The difference in the suspension level compared with a suspension level datum can be determined and an adjustment to the camera pose parameter values can be obtained from the difference in the suspension level.

Abstract: A method and a device are for detecting and reproducing a lateral and/or rear surrounding area of a vehicle with a trailer, so that the driver can monitor the surrounding area. The surrounding area is detected by at least one image capturing device which produces image data of the surrounding area. The image data is reproduced by at least one image reproducing device arranged in a field of vision of the vehicle driver. A characteristic of the reproduced image section is adapted dependent on an alignment of the trailer relative to the vehicle.

Abstract: The application provides a method of calibrating a camera of a vehicle. The vehicle has a reference frame. The method comprises taking an image of a scene by the camera. The ground plane of the vehicle is then determined according to features of the image. An origin point of the vehicle reference frame is later defined as being located on the determined ground plane. A translation of a reference frame of the camera is afterward determined for aligning the camera reference frame with the vehicle reference frame.

Abstract: An image recognition system and method for a vehicle, including at least two camera units, each being configured to record an image of a road in the vicinity of the vehicle and to provide image data representing the respective image of the road, a first image processor configured to combine the image data provided by the at least two camera units into a first top-view image. The first top-view image is aligned to a road image plane, a first feature extractor configured to extract lines from the first top-view image, a second feature extractor configured to extract an optical flow from the first top-view image and a second top-view image, generated before the first top-view image by the first image processor, and a curb detector configured to detect curbs in the road based on the extracted lines and the extracted optical flow and provide curb data representing the detected curbs.

Abstract: A method and device for determining an ego-motion of a vehicle are disclosed. Respective sequences of consecutive images are obtained from a front view camera, a left side view camera, a right side view camera and a rear view camera and merged. A virtual projection of the images to a ground plane is provided using an affine projection. An optical flow is determined from the sequence of projected images, an ego-motion of the vehicle is determined from the optical flow and the ego-motion is used to predict a kinematic state of the car.

Abstract: A method and a device for image stabilization of an image sequence of one or more cameras of a vehicle are disclosed. Images of the surroundings of the vehicle are recorded and image points of the recorded images are projected to a unit sphere, where the projection includes applying a lens distortion correction. For each of the one or more cameras, a vanishing point is calculated using the projected image points on the unit sphere and a motion of the vanishing point on the unit sphere is tracked. The motion of the vanishing point is used to calculate a corrected projection to a ground plane.

Abstract: A parking assistance system for a vehicle includes surround sensors, internal sensors, a data memory, and a processing unit. The surround sensors are configured to provide sensor data of the surrounding of the vehicle. The internal sensors are configured to detect a seat occupancy of passenger seats within the passenger room of the vehicle. The data memory stores a vehicle data model of the vehicle including positions of vehicle doors in the chassis of the vehicle. In addition, the processing unit is configured to process the sensor data received from the surround sensors to calculate an occupancy grid map of the vehicle's surrounding and further configured to calculate disembarking distances for the vehicle doors depending on the vehicle data model, the occupancy grid map and the detected seat occupancy.

Abstract: An enhanced object detecting method and apparatus is presented. A plurality of successive frames is captured by a monocular camera and the image data of the captured frames are transformed with respect to a predetermined point of view. For instance, the images may be transformed in order to obtain a top-down view. Particular features such as lines are extracted from the transformed image data, and corresponding features of successive frames are matched. An angular change of corresponding features is determined and boundaries of an object are identified based on the angular change of the features.

Abstract: A camera mounted on or for a vehicle has camera rotational parameters ?, ?, ? and camera translational parameters xc, yc, zc in a camera image coordinate system, and the vehicle has a vehicle coordinate system. A method for online or on-the-fly calibration of the camera involves two independent steps, namely while the vehicle is moving relative to the ground, calibrating the camera rotational parameters using a parallel geometrical calibration process, and calibrating at least some of the camera translational parameters xc, yc independently of the camera rotational parameters.

Abstract: A damage recognition assist system of a vehicle, the system includes at least one vehicle camera adapted to provide a stream of camera frames including camera images of the vehicle's surrounding and of areas or parts of the vehicle's chassis; a monitoring unit adapted to detect an imminent interaction of the vehicle with an external object and to identify areas or parts of the vehicle's chassis possibly affected by the detected interaction; a comparison unit adapted to compare recent camera images of the possibly affected areas or parts of the vehicle's chassis with previous camera images of the same areas included in reference camera frames to determine the state and level of damage of the vehicle's chassis due to the detected interaction.

Abstract: This disclosure relates to a method in which points common to two or more of the images that appear to represent the same real world features are identified, and changes in the location of the points between respective images are used to deduce the motion of the camera and to find the position of the real world features in three dimensional space. In order to determine the scale of the three dimensional information, the position of a reference feature, whose actual distance from the camera is known, is found from the images. The reference feature is found by considering only candidate points selected from candidate points falling within a portion of the image corresponding to a part of the field of view of the camera. The scale is determined from the distance between the camera and the reference feature in the image and in real life.

Abstract: A method for harmonizing a combined image involves receiving a respective image or image frame from each of two or more cameras, the images from two of the cameras representing the same region in an overlap region, measuring pixel statistics of at least some of the pixels in the overlap region for the image of each of the two cameras, determining a difference in the pixel statistics ?pixel of the image from each of the two cameras in the overlap region, calculating a correction factor Kpixel, wherein the correction factor Kpixel may be predicted to produce a reduction in ?pixel of less than ?pixel/2 when applied to the image of one of the two cameras, applying the correction factor Kpixel in a hardware device acquiring the image, and receiving a further image or image frame from each of the two cameras.

Abstract: A method for estimating ego motion of an object moving on a surface, the method including generating at least two composite top view images of the surface on the basis of video frames provided by at least one onboard video camera of the object moving on the surface; performing a region matching between consecutive top view images to extract global motion parameters of the moving object; calculating the ego motion of the moving object from the extracted global motion parameters of the moving object.

Abstract: A system (1) for calibrating an image capture device (2) mounted on a motor vehicle for offset from an ideal position and an ideal angular orientation while the vehicle is moving comprises selecting an image (12) in a captured image frame (10) of a stationary object relative to the motor vehicle, which is capable of being tracked through a plurality of successively captured image frames (14a,14d), predicting the locations at which the target image (12b) should appear in the respective successively captured image frames (14a,14d), comparing the actual location of the target image (12a) in the respective successively captured image frames (14a,14d) with the respective predicted locations (12b) and determining calibration values for the camera (2) from the results of the comparison, in the event that the actual (12a) and predicted locations (12b) of the target image in the respective image frames (14a,14d) do not coincide.