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: Methods and apparatus, including computer program products, for processing images recorded by a camera (202) monitoring a scene (200). A set of images (204, 206, 208) is received. The set of images (204, 206, 208) includes differently exposed images of the scene (200) recorded by the camera (202). The set of images (204, 206, 208) is processed by a trained neural network (210) configured to perform object detection, object classification and/or object recognition in image data, wherein the neural network (210) uses image data from at least two differently exposed images in the set of images (204, 206, 208) to detect objects in the set of images (204, 206, 208).

Abstract: A method and a digital video camera for reducing intensity variations in a video stream depicting a scene comprising capturing, using a first sensor setting, a first frame; detecting a change in intensity in a portion of the first frame, the portion represents a first area of the scene; determining a second sensor setting based on the first frame; capturing, using the second sensor setting, a second frame; creating a local tone mapping mask, wherein a local tone mapping in the first area of the scene is different from a local tone mapping in an area outside the first area, and wherein the local tone mapping in the area outside the first area is based on a relation between the first sensor setting and the second sensor setting; and applying the local tone mapping mask to the second frame.

Abstract: A method and a digital video camera for reducing intensity variations in a video stream depicting a scene comprising capturing, using a first sensor setting, a first frame; detecting a change in intensity in a portion of the first frame, the portion represents a first area of the scene; determining a second sensor setting based on the first frame; capturing, using the second sensor setting, a second frame; creating a local tone mapping mask, wherein a local tone mapping in the first area of the scene is different from a local tone mapping in an area outside the first area, and wherein the local tone mapping in the area outside the first area is based on a relation between the first sensor setting and the second sensor setting; and applying the local tone mapping mask to the second frame.