Abstract: A method (1200) generating a three-dimensional (3D) representation of a real environment is provided. The method (1200) is performed by an apparatus (1300). The method (1200) comprises obtaining (s1202) a first image representing a first portion of the real environment, obtaining (s1204) a second image representing a second portion of the real environment, and identifying (s1206) a contour within the first image. The method also comprises identifying (s1208) a first cluster of key points from an area included within the contour, using at least some of the first cluster of key points, and identifying (s1210) a second cluster of key points included in the obtained second image.

Abstract: There is provided mechanisms for joint visual object detection and object mapping to a 3D model. A method is performed by an image processing device. The method includes obtaining a first sequence of digital images of a scene as captured by a first image capturing unit, and obtaining a second sequence of digital images of the scene as captured by a second image capturing unit. The second sequence of digital images is time-wise synchronized with the first sequence of digital images by being captured time-wise in parallel with the first sequence of digital images. The first image capturing unit has a narrower field of view than the field of view of the second image capturing unit. The first image capturing unit and the second image capturing unit have a known spatial relation. The method includes performing joint visual object detection and object mapping to the 3D model.

Abstract: Object parts (20, 21, 22, 23, 24) are detected in a picture using object detector(s) (3) and part location representations (40, 42, 43, 44) are generated for the detected object parts (20, 22, 23, 24). The size of an object (10) comprising object parts (20, 21, 22, 23, 24) is estimated based on a geometric model and the part location representations (40, 42, 43, 44). Search locations (51) in the picture for a search window (52) having a size based on the estimated size are determined based on the part location representations (40, 42, 43, 44). The search locations (51) are then processed by identifying any detected object part (20, 22, 23) that is within the search window (52) positioned at the search location (51). A homography is estimated by minimizing an error between mapped object part(s) from the geometric model and the identified detected object part(s) (20, 22, 23). If the error is smaller than a threshold value, an object location representation is determined for the object (10).

Abstract: Audio streams are received from participant computing devices of a participant cohort that includes a plurality of participant computing devices that are each located in a same area and are each connected to a teleconference. A first audio stream is selected from the two or more audio streams for broadcast based at least in part on signal quality information indicative of a signal quality associated with each of the two or more audio streams and/or historical signal quality information indicative of a signal quality associated with prior audio streams received from the two or more participant computing devices. The first audio stream is broadcasted to one or more participant computing devices different than the plurality of participant computing devices of the participant cohort.

Abstract: There is provided mechanisms for identifying decreasing object detectability for a video stream. A method is performed by a controller. The method includes receiving the video stream over a communication channel. The method includes receiving first visual object detection (VOD) data of the video stream. The first VOD data has been obtained from the video stream before is sent over the communication channel towards the controller. The method includes obtaining second VOD data of the video stream. The second VOD data is obtained from the video stream after is received by the controller. The method includes determining, by comparing the first VOD data to the second VOD data, whether any object present in the first VOD data has a decreased object detectability in the second VOD data, thereby identifying the decreasing object detectability for the video stream. The method includes issuing a notification when the decreasing object detectability has been identified.

Abstract: A method for detecting a change in an area of interest. The method includes: 1) forming a pointcloud that comprises: i) a first set of points derived from a first set of one or more images and ii) a second set of points derived from a second set of one or more images; 2) for each point included in the pointcloud, assigning a similarity score to the point; 3) for at least each point from the pointcloud that is included in a particular set of points (e.g., for each point in the pointcloud that is assigned a similarity score that exceeds a threshold (e.g. 0)), determining a value Nt and a value Nnew and adjusting the similarity score assigned to the point based on Nt and Nnew; and 4) using the adjusted similarity scores to determine whether the area of interest has changed.

Abstract: A method (900) for estimating boundaries of an OOI. The method includes obtaining (s902) a first point cloud comprising a set of N points. The method also includes obtaining (s904) a set of K images. The method also includes, for each point included in the set of N points, identifying (s906), for each one of the K images, the point's location within a 2D space corresponding to the image, thereby obtaining, for each point included in the set of N points, K location identifiers. The method also includes, for each point included in the set of N points, determining (s908) a motion metric for the point using the K location identifiers for the point. The method also includes using (s910) the N motion metrics to form a subset of the N points. The method also includes, for each point included in the subset of points, obtaining (s912) a location identifier for the point.

Abstract: There is provided mechanisms for joint visual object detection and object mapping to a 3D model. A method is performed by an image processing device. The method includes obtaining a first sequence of digital images of a scene as captured by a first image capturing unit, and obtaining a second sequence of digital images of the scene as captured by a second image capturing unit. The second sequence of digital images is time-wise synchronized with the first sequence of digital images by being captured time-wise in parallel with the first sequence of digital images. The first image capturing unit has a narrower field of view than the field of view of the second image capturing unit. The first image capturing unit and the second image capturing unit have a known spatial relation. The method includes performing joint visual object detection and object mapping to the 3D model.

Abstract: A method (1200) for producing a reduced point cloud comprising an object of interest (OOI) from an original point cloud comprising the OOI.

Abstract: A method for detecting a change in an area of interest. The method includes: 1) forming a pointcloud that comprises: i) a first set of points derived from a first set of one or more images and ii) a second set of points derived from a second set of one or more images; 2) for each point included in the pointcloud, assigning a similarity score to the point; 3) for at least each point from the pointcloud that is included in a particular set of points (e.g., for each point in the pointcloud that is assigned a similarity score that exceeds a threshold (e.g. 0)), determining a value Nt and a value Nnew and adjusting the similarity score assigned to the point based on Nt and Nnew; and 4) using the adjusted similarity scores to determine whether the area of interest has changed.

Abstract: There is provided mechanisms for visual object detection in a sequence of images. A method is performed by a visual object detector (200). The method comprises obtaining (S102) a sequence of images of a scene. The sequence of images at least comprises a current image of the scene and a previous image of the scene. The method comprises extracting (S104) a set of objects from the sequence of images by performing visual object detection in the sequence of images. Performing the visual object detection in at least part of the current image is conditioned on a set of conditions being fulfilled. The set of conditions at least pertains to an image-wise descriptor classification score computed for at least one of the previous image and the current image and pertaining to which type of content the scene comprises, and an image overlapping score pertaining to how much overlap in image area there is between the previous image and the current image.

Abstract: A method performed by an image registration entity includes obtaining a matching between a first set of objects in a first image of a scene and a second set of objects in a second image of the scene, and obtaining a first set of key-points from the first image and a second set of key-points from the second image. Image registration is performed by matching the first set of key-points to the second set of key-points. Those of the first set of key-points that are mapped to objects in the first set of objects and that have matching objects in the second set of objects are restricted to only be matched to those of the second set of key-points that are mapped to any of the matching objects in the second set of objects. The matching between key-points resulting from the image registration is applied when constructing an image representation of the scene.

Abstract: Object parts (20, 21, 22, 23, 24) are detected in a picture using object detector(s) (3) and part location representations (40, 42, 43, 44) are generated for the detected object parts (20, 22, 23, 24). The size of an object (10) comprising object parts (20, 21, 22, 23, 24) is estimated based on a geometric model and the part location representations (40, 42, 43, 44). Search locations (51) in the picture for a search window (52) having a size based on the estimated size are determined based on the part location representations (40, 42, 43, 44). The search locations (51) are then processed by identifying any detected object part (20, 22, 23) that is within the search window (52) positioned at the search location (51). A homography is estimated by minimizing an error between mapped object part(s) from the geometric model and the identified detected object part(s) (20, 22, 23). If the error is smaller than a threshold value, an object location representation is determined for the object (10).

Abstract: There is provided mechanisms for identifying decreasing object detectability for a video stream. A method is performed by a controller. The method includes receiving the video stream over a communication channel. The method includes receiving first visual object detection (VOD) data of the video stream. The first VOD data has been obtained from the video stream before is sent over the communication channel towards the controller. The method includes obtaining second VOD data of the video stream. The second VOD data is obtained from the video stream after is received by the controller. The method includes determining, by comparing the first VOD data to the second VOD data, whether any object present in the first VOD data has a decreased object detectability in the second VOD data, thereby identifying the decreasing object detectability for the video stream. The method includes issuing a notification when the decreasing object detectability has been identified.