Abstract: A method for recognizing one or more objects represented in an image by an M-dimensional point cloud, composed of n points, includes: determining, for each of a number m>0, of specific one-dimensional variables, an associated value of the variable for each of the points on the basis of the position or properties of the point; determining, for each of the variables, a frequency distribution with respect to the respective values of said variable; approximating each of the frequency distributions by a linear combination of a finite number of one-dimensional probability density functions associated with the variable; segmenting the image such that each product of in probability density functions is uniquely assigned a segment of the image; assigning each point of the point cloud to the segment; and identifying at least one of the segments to which at least a predefined minimum number of points was assigned.

Abstract: A method for identifying an object of interest of a person based on that person's detected behavior is disclosed. The method involves receiving or generating ambient data representing a first time sequence of ambient data items, each representing for a specific respective point in time in the first time sequence ambient targets being currently present as a respective object in the spatial environment of the person at that point in time, receiving or generating directional sensor data representing a second time sequence of directional data items, each representing directional measurements relating to a specific point in time in the second time sequence, matching the directional sensor data to the possible targets of the ambient data to generate a third time sequence of result data items, and classifying one or more of the ambient targets based on at least two of the corresponding result data items of the third time sequence.

Abstract: A method for distinguishing different configuration states of an object based on an image representation of the object involves acquiring original image data representing an original image comprising a picture of the object; detecting the object within the original image; determining an orientation of the detected object; determining a region-of-interest within the original image representing the picture of the object at least in parts; generating normalized image data representing a two-dimensional normalized image by transforming the ROI of the original image based on the determined orientation of the object to a reference frame in which the ROI is represented by the normalized image with a predefined standard orientation and scaling; extracting characteristic features of the object as represented by the normalized image data; and classifying the extracted characteristic features to determine a specific configuration state of the object associated with the classification.

Abstract: A method for the automated detection of a seat occupancy state of a seating arrangement involves receiving or generating measurement data representing an associated radar point cloud for each measurement frame from a plurality of measurement frames; and determining a seat occupancy state of the seating arrangement based on an evaluation model, which returns, as a function of at least two groups of radar point clouds, one of a plurality of predefined possible seat occupancy states of the seating arrangement as an evaluation result. For each radar point cloud, the set of its radar points is segmented into clusters in order to individually assign to each of the seats one of the clusters located spatially closest to it. A stored seat occupancy state is updated based on the evaluation result only if the value is less than a threshold value.

Abstract: The invention relates to a method for automatically estimating the body weight of a person, comprising the following steps: generating or receiving image data representing an image, captured by an image sensor, of at least one sub-area of the body of a person by means of pixels; classifying at least one subset of the pixels based on a classification, in which different classes each correspond with another body area, wherein the pixels to be classified are each assigned to a determined body area of the person and respective confidence values are determined for these class assignments; for each of at least two of the classes with assigned pixels, calculating a position of at least one reference point, determined according to a specification, for the body area corresponding with this class, based on the pixels assigned to this class; determining a respective distance between at least two of the selected reference points; determining at least one estimation value for the body weight of the person based on a prede

Abstract: A method for identifying an object of interest of a person based on that person's detected behavior is disclosed. The method involves receiving or generating ambient data representing a first time sequence of ambient data items, each representing for a specific respective point in time in the first time sequence ambient targets being currently present as a respective object in the spatial environment of the person at that point in time, receiving or generating directional sensor data representing a second time sequence of directional data items, each representing directional measurements relating to a specific point in time in the second time sequence, matching the directional sensor data to the possible targets of the ambient data to generate a third time sequence of result data items, and classifying one or more of the ambient targets based on at least two of the corresponding result data items of the third time sequence.

Abstract: A system and method are disclosed relating to a computer system that estimates the status of the driver of a vehicle. The system comprises the following steps: Acquisition of an image from a depth sensor containing depth and optional an IR intensity image and an RGB color image; identification of pixels that belong to the drivers head; creation of a 3D model of the head including an intensity model and a variability estimate for depth, grayscale and color information; estimation of the principal head pose and the neutral facial expression; estimation of the current relative head pose with respect to the principal head pose; identification of pixels that do not match the neutral face model with respect to depth, grayscale or color information or any combination thereof; clustering of the pixels with identified deviations; classification of spatial and temporal patterns to identify driver status and distraction events.

Abstract: Described herein are a system and method for identifying extremities of an object within a scene. The method comprises operating an image processing system to receive image data from a sensor. The image data represents an image of the scene with the object. The image data comprises a two-dimensional array of pixels and each pixel contains a depth value indicating distance from the sensor. The image processing system slices the image into slices. Each respective slice comprises those pixels with depth values that lie within a respective range of distances defined relative to a reference. For each of the slices, the method identifies one or more connected regions of pixels that are neighbors in the two-dimensional array of pixels. The method builds, based on the connected region of pixels that have been identified for the slices and depth information inherent to the respective slices, a graph consisting of interconnected nodes.