Abstract: A computer-implemented method in a processor device of a camera, the method comprising: acquiring image frames comprising video data, communicating with a receiver device for continuously transmitting a video stream to the receiver device over a communication network; detecting at least one object in the image frames of the video data, the detected at least one object belong to at least one predetermined object class selected as surveillance target; cropping sub-areas in the image frames of the video data, the sub-areas including the at least one detected object, and adding the cropped sub-areas to image frames of the video stream being continuously transmitted as a single video stream to the receiver device.

Abstract: A method for fusing a primary image (P) and a secondary image (S) into a target image comprises determining a local noise level of a region of the primary image; deriving a low-frequency, LF, component (PLF) from said region of the primary image and a high-frequency, HF, component (SHF) from a corresponding region of the secondary image, wherein the LF and HF components refer to a common cut-off frequency; combining the LF component and the HF component into a target image region, wherein the HF component's relative contribution to the target image region increases gradually with the local noise level; and repeating the preceding operations and merging all output target image regions thus obtained into the target image. A system implementing said method is also provided.

Abstract: A first image is captured by the camera, using a first focus setting and a first aperture size. A first protrusion focus measure in a protrusion area of an object in the first image and a first recess focus measure in a recess area of the object in the first image are determined. A second image is captured by the camera, using the first focus setting and a second aperture size, and the object is detected. A second protrusion focus measure and a second recess focus measure are determined in the second image. A protrusion focus difference between the first and second protrusion focus measures, and a recess focus difference between the first and second recess focus measures are calculated. The protrusion focus difference and the recess focus difference are compared and if they differ by less than a predetermined threshold amount, it is determined that the object is fake.

Abstract: A method for stray light compensation is disclosed. The method comprising: acquiring a first image with a first imaging device covering a first field-of-view; acquiring a second image with a second imaging device covering a second field-of-view, wherein the second field-of-view is larger than the first field-of-view and wherein the first field-of-view is included in the second field-of-view; estimating stray light components in pixels of the first image from pixel data of pixels in the second image; and compensating for stray light in the first image by subtracting the estimated stray light components in pixels of the first image. Also, a system for stray light compensation is disclosed.

Abstract: A method of configuring a camera comprising: collecting a video data with the camera, providing a plurality of imaging profiles, each imaging profile being associated with a set of scene characteristics, for each imaging profile, generating a spatial output data in dependence on the video data and the set of scene characteristics of the imaging profile, wherein the spatial output data is indicative of spatial locations of events detected in the video data matching one or more of the scene characteristics, performing a comparison of the spatial output data of each of the plurality of imaging profiles, selecting a preferred imaging profile in dependence on the comparison, configuring the camera to operate according to the preferred imaging profile.

Abstract: A method for determining whether a video camera is out-of-focus comprises: capturing image data; setting a focus setting difference threshold based on a filter function configured to filter the image data; adjusting a focus setting between a present focus setting and an altered focus setting such that a first subset of the image data is captured with the video camera being set in the present focus setting and such that a second subset of the image data is captured with the video camera being set in the altered focus setting, wherein the adjusting is performed such that a difference between the present and altered focus settings is below the threshold; forming the video stream by applying the filter function on the image data; determining first and second focus metrics of the image data; and determining whether the video camera is out-of-focus by comparing the first and second focus metrics.

Abstract: A video processing pipeline comprises: a video frame partitioning module configured to divide video frames into frame portions; a change module configured to, for each frame portion of a second video frame, determine a difference between video data of the frame portion of the second video frame and video data of a corresponding frame portion of a first video frame; a probability module configured to determine a probability, that decreases with an increasing size of the difference, for individually suppressing each of the determined differences; a suppress module configured to decide whether to suppress each determined difference or not based on a respective determined probability, and to suppress the difference in case it is decided to suppress the difference; and an encode module configured to inter-encode the frame portions of the second video frame in relation to the first video frame.

Abstract: The present invention relates generally to auto focus algorithms for setting a focus of a camera. More specifically, the present invention relates to a method (600) and device for setting a focus of a camera wherein a camera motion level of the camera is continuously measured (S604) during the read-out of pixel data by a rolling shutter image sensor and wherein the auto focus algorithm uses weighted focus measures which are calculated (S612) using the continuously measured camera motion levels.