Abstract: In a method (30) for obtaining information (18a, b) about a potential object (8a, b) in a camera image (10) of a scene (2), A) the camera image (10) is provided as a single 2D camera image, B) potential objects (8a, b) are detected in the camera image (10), C) a frame projection (14a, b) of a 3D frame (16) directly surrounding the object (8a, b) into the camera image (10) is determined for at least one of the detected objects (8a, b) as at least part of the information (18a, b), wherein steps B) and C) are each performed with the aid of a neural network (32).

Abstract: A method for recognizing an object from input data is disclosed. Raw detections are carried out in which at least one attribute in the form of a detection quality is determined for each raw detection. At least one further attribute for each raw detection is determined. A temporally or spatially resolved distance measure is determined for at least one attribute of the raw detections. Raw detections of a defined distance measure are combined to form a group of raw detections. The object is recognized from a group with at least one raw detection with the smallest distance measure of the at least one attribute in comparison with another raw detection, or from a group with at least one raw detection which were combined by combining at least two raw detections with the smallest distance measure of the at least one attribute to form said one raw detection.

Abstract: A learning-based method for estimating self-movement and items of distance information in a camera system having at least two cameras, according to which temporally successive individual images produced by each camera during the movement of the camera system are supplied as input images to a self-monitored deep neural network of a deep learning system. According to the method, the self-monitored neural network is trained using these individual images. Moreover, in the course of the inference the deep learning system produces, from the input images, output data that describe the movement of the camera system.

Abstract: A method for configuring an object detection system includes providing annotated training data comprising image data with defined assignments to at least one object, and training a neural network with a first neural sub-network, which is provided to compress the image data. The first neural sub-network is connected to at least one further neural sub-network. The at least one further neural sub-network is configured to detect an object from the compressed training data. The first neural sub-network is parameterized in such a manner that the object is detected using the at least one further sub-network in a defined quality. The neural sub-networks are trained jointly.

Abstract: A method for recognizing an object from input data is disclosed. Raw detections are carried out in which at least two objects are determined. At least one relational attribute is determined for the at least two objects. The at least one relational attribute defines a relationship between the at least two objects. An object is recognized taking account of the at least one relational attribute.

Abstract: A method for recognizing an object from input data is disclosed. Raw detections are carried out in which at least one attribute in the form of a detection quality is determined for each raw detection. At least one further attribute for each raw detection is determined. A temporally or spatially resolved distance measure is determined for at least one attribute of the raw detections. Raw detections of a defined distance measure are combined to form a group of raw detections. The object is recognized from a group with at least one raw detection with the smallest distance measure of the at least one attribute in comparison with another raw detection, or from a group with at least one raw detection which were combined by combining at least two raw detections with the smallest distance measure of the at least one attribute to form said one raw detection.

Abstract: A method and device for analyzing an image and providing the analysis for a driving assistance system of a vehicle, including recording the image; determining an operating state of the vehicle; analyzing the image using at least one image analysis method that is selected from at least two possible image analysis methods as a function of the operating state of the vehicle; and providing the analysis of the image as data values for the driving assistance system.

Abstract: A method and device for analyzing an image and providing the analysis for a driving assistance system of a vehicle, including recording the image; determining an operating state of the vehicle; analyzing the image using at least one image analysis method that is selected from at least two possible image analysis methods as a function of the operating state of the vehicle; and providing the analysis of the image as data values for the driving assistance system.