Abstract: A method of enabling a remote access to a first network device from a second network device includes the second device generating a data item signed by a network service trusted by the first device, and including a fingerprint of a public key of the second device. The signed data item is sent to the first device via a signalling service, SIGS, as part of a negotiation of terms for a peer-to-peer connection. The first device uses the received signed data item to verify that the terms it receives from the SIGS has not been tampered with by the SIGS, in order to prevent the SIGS from performing a Man-In-The-Middle attack. Various network devices and a network system are also provided.

Abstract: A method detects one or more occluded areas of a scene analysed by an object tracking system. The method includes building a map of one or more occluded areas in a scene. Building the map comprises running a re-identification algorithm on a video sequence to try to resume a lost object track. If the object track is successfully resumed, the method includes determining an area of the scene where the first object track is lost and an area of the scene where the first object track is resumed. A connection between the first and the second area of the scene is added the map such that the map identifies that an object track being lost in the first area of the scene has been resumed in the second area of the scene.

Abstract: A method for determining images plausible to have a false negative object detection comprises providing a group of historic trajectories, wherein each historic trajectory comprises a reference track that represents one or more historic tracks and comprises an object class of historic object detections that belong to the one or more historic tracks; performing tracking; performing object detection; for a determined track that does not match any determined object detection, comparing the determined track with reference tracks of historic trajectories for identifying a matching reference track; upon identifying a matching reference track, defining images of the determined track as being plausible to have a false negative object detection for the object class of the historic trajectory comprising the matching reference track; and upon not identifying a matching reference track, defining the determined track as a false positive track.

Abstract: A method for feature extraction of detected objects, comprising the steps of: receiving a plurality of images, each depicting an object detected by the object detecting application; concatenating the plurality of images into a composite image according to a grid pattern; feeding the composite image through a convolutional neural network (CNN) trained for feature extraction, wherein each convolutional layer of the CNN is configured to, while convolving input data to the convolutional layer using a convolutional filter: determine a currently convolved image of the plurality of images by determining a centre coordinate of a subset of the input data currently covered by the convolutional filter, and mapping the centre coordinate to the grid pattern; and selectively nullifying all weights of the convolutional filter that cover input data derived from any of the plurality of images not being the currently convolved image.

Abstract: A method for thermal image processing, comprises: acquiring a thermal image depicting a scene; obtaining a frequency distribution based on pixel intensities of the thermal image; processing the frequency distribution to determine whether the frequency distribution comprises a peak, caused by a hot object in the scene, which is separated from a thermal background of the scene by more than an intensity threshold; and in response to determining that the frequency distribution comprises the peak, processing the thermal image to suppress a ghost image of the hot object in the thermal image, wherein the ghost image is caused by internal reflections of radiation from the hot object in the thermal camera, and wherein the processing of the thermal image comprises: estimating a location of ghost image pixels forming the ghost image; and suppressing the ghost image in the thermal image by adjusting intensities of the ghost image pixels.

Abstract: A method, system and software for determine a color of a tracked object. Using a first video sequence and foreground objects detected therein, a color rendering metric may be determined for each area of a plurality of areas in the scene. Such color rendering metrics may then be used in case a tracked object is determined to have different colors in different images of a second video sequence, such that the colors detected in an area associated with a higher color rendering metric is selected over an area associated with a lower color rendering metric.

Abstract: The present system and method generally relate to the field of camera surveillance, and in particular to object re-identification in video streams captured by a camera.

Abstract: A method of processing digital video data comprises continuously capturing digital video data representing image frames. While capturing the digital video data, the digital video data is encoded into a sequence of encoded image frames, the sequence comprising key frames and delta frames, and storing the sequence of encoded image frames. It is then determined that the stored sequence of encoded image frames is to be entropy coded and, as a consequence, entropy coding the sequence of encoded image frames into an entropy coded sequence of image frames and storing the entropy coded sequence of image frames.

Abstract: A method for adding at least one additional image frame to a stream of encoded image frames, the stream of encoded image frames comprising image frames encoded by an encoder. An event relating to at least one of: i) a change of operation state of a sender of the stream of encoded image frames, or ii) a change of connection state between the sender and a receiver is detected. Depending on the type of the detected event, the at least one additional image frame is provided. Each at least one additional image frame comprises a reference to a preceding image frame and codes a predetermined visual change relative to the preceding image frame. The predetermined visual change is indicative of the type of the detected event. The at least one additional image frame is added to the stream of encoded image frames to obtain a combined stream of image frames.

Abstract: Techniques for Long Term Reference (LTR) frame updating in a video encoding process are performed by an image processing device as part of the video encoding process. The method comprises encoding a first LTR frame. The method comprises encoding a plurality of frames referencing directly or indirectly to the first LTR frame. The method comprises sequentially updating the first LTR frame by evaluating a cost for encoding a block of image data in one of the plurality of frames and by updating an image area in the first LTR frame when the cost fulfils a cost criterion. The image area is updated based on the block of image data in at least one of the plurality of frames. The method comprises encoding the sequentially updated first LTR frame as a second LTR frame.

Abstract: A method of detecting a change of ratio of occlusion of a tracked object in a video sequence. For each of a plurality of image frames, a bounding box of the tracked object is determined and for each pair of successive image frames, an intersection over union (IoU) of a first bounding box in a first image frame and a second bounding box in a second image frame is calculated. Similarly, for a further pair of successive image frames, a further IoU of a first bounding box in a first image frame of the further pair of successive image frames and a second bounding box in a second image frame of the further pair of successive image frames is calculated. If the further IoU differs from the calculated IoUs by more than a threshold amount, the ratio of occlusion of the tracked object has changed.

Abstract: The present disclosure relates to a method of background modelling for a video stream acquired by a camera having movement capabilities. The method comprises: acquiring a video stream; repeatedly updating a background model for the video stream by analyzing changes in the sequence of image frames and categorizing image areas in the image frames which do not change over time as background; detecting camera movement; performing a reset of the background model by: applying an image segmentation and/or object detection algorithm to identify at least foreground objects; after having performed the reset of the background model, returning to repeatedly updating the background model for the video stream by analyzing changes in the sequence of image frames and categorizing image areas in the image frames which do not change over time as background. The disclosure further relates to an image processing system.

Abstract: A camera arrangement comprises a camera head, a holder ring and a holder base. The camera head has an imaging unit and an interaction portion with constant cross section. The camera holder ring has an inner surface configured to fit onto the interaction portion of the camera head, and the camera holder base has a receptacle orifice dimensioned to receive the camera holder ring fitted onto the interaction portion of the camera head and grip an outer surface of the holder ring. The receptacle orifice has reduced dimensions due to a tightening function to fixate the camera holder ring and the camera head in relation to the camera holder base, and thereby to fixate an orientation of the camera in relation to the camera holder base. The camera holder ring is movable along a length of the interaction portion until the tightening function of the receptacle orifice has been actuated.

Abstract: A method for encoding lidar data where subsequent frames of lidar data to be encoded are received. Each frame of lidar data comprises a number of lidar return signals for each of a plurality of rays emitted at a respective elevation and azimuth angle by a lidar, and each lidar return signal includes lidar measurement values. Each frame of lidar data is then represented as an image frame of a video sequence, wherein, for each ray of the plurality of rays of the frame of lidar data, lidar measurement values of different lidar return signals are represented in different image portions of the image frame. The different image portions are stacked after each other in a row direction or a column direction of the image frame. The video sequence is then encoded using video encoding.