Axis Ab

Cognimatics Ab

Spiideo Ab

Håkan Ardö

Rikard Berthilsson

Karl Åström

Klas Josephson

Magnus Linderoth

Song Yuan

Carl-Axel Alm

Niclas Danielsson

Axel Keskikangas

Mikael Nilsson

Marcus Johansson

Tim Borglund

Raphael Teller

Siri Dovner

Jonna Hellstrom

Martin Ahrnbom

Karl Erik Åström

Jonna HELLSTRÖM

A plurality of feature vectors for moveable objects are received in a sequence of image frames captured during a first period. The feature vectors are received from a machine learning module trained to extract similar feature vectors in different image frames. An initial value is assigned to a first feature vector , or to a cluster of feature vectors identified in a second sequence of image frames preceding the first image frame. The indicator indicates whether vector is alive. For each subsequent image frame , the indicator is updated by: updating the value based on a similarity between the feature vector and the first feature vector or the cluster of feature vectors. If the value of the indicator indicates that the vector is alive, determining that there is a moveable object that is located in the captured scene at least the predetermined portion of the given period of time.

Device and method for determining whether there is a moveable object located in a scene at least a predetermined portion of a given period of time

A method for anonymizing a digital colour image comprising obtaining the digital colour image, and applying a linear random function to a respective colour vector representing colour components of a respective pixel of the digital colour image to obtain a monochrome image. The linear random function varies over the pixels of the digital colour image, and is further dependent on at least one random parameter.

Method and image-processing device for anonymizing a digital colour image

An image processor and a method therein to provide a target image for evaluation with an object detector. The method comprises: obtaining a source image captured by a camera and depicting an object, and applying an inverse pixel transform to each target pixel of a target image to determine one or more source pixels located at a position in the source image corresponding to a position of each target pixel in the target image. The method further comprises assigning, to each target pixel, a target pixel value determined based on one or more source pixel values of the determined one or more source pixels located at the corresponding position, thereby is a size c in the target image of the depicted object of a specific object type normalized in at least one size dimension. Thereafter, the target image is fed to an object detector for evaluation.

Image processor and a method therein for providing a target image

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 system for providing virtual pan-tilt-zoom, PTZ, video functionality video to a plurality of users (-) over a data network () is provided. The system () comprises one or more network video cameras (-) positioned to make a video recording of a real-world target field (), or a respective subarea thereof, and output a respective raw video segment onto the data network (), a back-end camera calibration unit () which is adapted to obtain a relative position and orientation of the respective network video camera (-) with respect to a three dimensional world space for the target field (), and to save the obtained relative position and orientation as metadata for the respective network video camera (-), a back-end video processing unit (), connected to the data network () and adapted to receive the respective raw video segment(s) recorded by the network video camera(s) (-), and to store or buffer video contents of the respective raw video segment(s) in a back-end video storage (), a back-end video streaming unit () adapted to stream the stored or buffered video contents and the saved metadata for the respective network video camera (-) onto the data network (), a plurality of client devices (-) for use by the plurality of users (-). Each client device (-) is adapted to receive the streamed video contents and the metadata, determine a chosen virtual pan-tilt-zoom value and present, on a display (-) of the client device, the video contents by projecting directions, in the three dimensional world space, onto the display (-) based on the chosen virtual pan-tilt-zoom value and the metadata.

System and method for providing virtual pan-tilt-zoom, PTZ, video functionality to a plurality of users over a data network

A system for providing virtual pan-tilt-zoom, PTZ, video functionality video to a plurality of users over a data network is provided. The system includes one or more network video cameras positioned to make a video recording of a real-world target field, or a respective subarea thereof, and output a respective raw video segment onto the data network, a back-end camera calibration unit which is adapted to obtain a relative position and orientation of the respective network video camera with respect to a three dimensional world space for the target field, and to save the obtained relative position and orientation as metadata for the respective network video camera, a back-end video processing unit, connected to the data network and adapted to receive the respective raw video segment(s) recorded by the network video camera(s), and to store or buffer video contents of the respective raw video segment(s) in a back-end video storage, a back-end video streaming unit adapted to stream the stored or buffered video contents and the saved metadata for the respective network video camera onto the data network, a plurality of client devices for use by the plurality of users.

There is provided a method and system for tracking a plurality of objects in a sequence of images. The method comprises: receiving an image from a sequence of images; calculating, based on the received image, a detection probability map which, for each state of a predefined set of states of an object, specifies a probability that any of the plurality of objects is detected in that state in the received image; updating, based on the calculated detection probability map, an object identity map and an accumulated probability map recursively from a previous object identity map and a previous accumulated probability map corresponding to a previously received image of the sequence of images, and tracking each object in the received image based on the updated object identity map and the updated accumulated probability map.

Method and system for tracking a plurality of objects in a sequence of images

A method and arrangement for calibrating stereo cameras. A first image from a first camera is received, and a second image is received from a second camera, the first image and the second image comprise an overlap. One or more candidate points of the first image and of the second image are determined in the overlap. The candidate points of the first image are matched with the candidate points of the second image and pairs of corresponding candidate points are determined. A homography of the first camera is calculated based on the pairs of corresponding candidate points and a homography of the second camera is calculated based on the pairs of corresponding candidate points. The first camera and the second camera are calibrated by adjusting an intrinsic parameter or an extrinsic parameter of each respective camera, based on the calculated homography of each respective camera.

Method and arrangement for calibration of cameras

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).

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Lund, Sweden

Håkan Ardö