Combain Mobile Ab

Cognimatics Ab

Axis Ab

Uman Sense Ab

Karl Åström

Håkan Ardö

Rikard Berthilsson

Björn Martin Gunnar Lindquist

Anders Mannesson

Mikael Nilsson

Marcus Johansson

Björn Lindquist

Sven Rasmus Ljungberg

Simon Emil Sten Burgess

Jonna Hellstrom

Martin Ahrnbom

Petter Ericson

Johan WASSÉLIUS

Jonna HELLSTRÖM

An imaging system is described having at least three cameras and a processing unit. The at least three cameras have a common field of view and camera centres positioned along a line. The at least three cameras are configured to image a calibration object to generate a set of calibration object images, wherein the calibration object is located nearby the at least three cameras and in the common field of view. The at least three cameras are further configured to image a scene comprising a set of distant scene position points to generate a set of position point images. The processing unit is configured to generate a set of calibration parameters in dependence on the set of calibration object images and the set of position point images.

Imaging system and a method of calibrating an image system

A method for generating an indoor environment model of a building. The method comprises forming a transmitter location model for defining positions of transmitters in said building using gathered information for establishing transmitter locations. The method further comprises receiving signal strength indicative measurements determined for a number of transmitters using at least one electronic communications device wherein the signal strength indicative measurements are based on a signal which has varying signal propagation characteristics in the indoor environment The signal strength indicative measurements are acquired from a number of known locations in the building, identifying discrepancies of signal transmittance in said indoor environment based on said signal strength indicative measurements in relation to said transmitter location model. The method further comprises determining locations of signal hindering elements causing said discrepancies and generating said indoor environment model including transmitter locations and said signal hindering elements.

Method for generating an indoor environment model and a method for determining position data for a location in an indoor environment

A method in a movable device for locating or positioning of the movable device within an environment comprises: receiving, from an external unit, a central environment model; determining distance-dependent measurements for one or more positions of the movable device; determining a location of the movable device based on the received central environment model and the determined distance-dependent measurements, said determining comprising: forming a local environment model defining estimated locations of the wireless transmission sources and of the one or more positions of the movable device, wherein the estimated locations of the wireless transmission sources in the local environment model are updated in relation to the central environment model, wherein the updated estimated locations of the wireless transmission sources and the estimated locations of the one or more positions of the movable device are set based on a calculated error of the local environment model in relation to the determined distance-dependent measurements.

Method and system for locating a position of a movable device

A method for generating a model of an environment enabling positioning within the environment is disclosed. The method comprises: receiving distance-dependent measurements relating to a distance between a receiving unit position and a source position; receiving information relating to geographic positions, each defining a receiving unit position or a source position; forming a candidate model, which defines candidate source positions and candidate receiving unit positions; iteratively reducing a model error of said candidate model, wherein said model error comprises a weighted contribution from distance-dependent errors, which are based on a difference between a distance-dependent value between a candidate source position and a candidate receiving unit position and the corresponding distance-dependent measurement, and geographic position errors, which are based on a difference between a geographic position and a corresponding candidate position, and outputting, from said iterative reducing, determined locations for said plurality of sources to form said model of the environment.

Generating a model for positioning

An apparatus being adapted to determine an occupancy in a designated area, said apparatus comprising a memory and a controller, said controller being configured to determine at least one passage time; wherein said passage time is a time for a detected passage into and/or out of said area based on said image stream, determine an average occupancy time, determine if a passage into said designated area is estimated to occupy the designated area by comparing the time for the passage with the average occupancy time, and determine an estimation of the present occupancy in the designated area to equal the number of such passages.

System and method for occupancy estimation

A method for tracking objects in a scene being viewed by a sequence of digital images comprises: separating a region of interest in the digital images into a plurality of spatially smaller, overlapping modules, and defining a hidden Markov model for each module. The method further comprises observing detections of positions of possible objects at different times, considering a set of states at a point of time (t) such that the set of states comprises a state having a global optimum probability, and backtracking through the sequence of digital images for each hidden Markov model to shrink the set of states that are possible for earlier parts of the sequence of digital images, such that a single optimal state is found for an earlier point of time (t&#x3c;t), whereby tracks of detected objects through the scene are determined up to the earlier point of time (t).

Method for tracking objects in a scene

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Lund, Sweden

Karl Åström