Abstract: A method for prioritization of video processing resources in a video processing and/or management system is provided. The method includes receiving video streams from multiple cameras and automatically ranking each camera as either a higher-priority or lower-priority camera. For higher-priority cameras, video content analysis is performed (as background processes), and the result is stored in storage. For lower-priority cameras, no such analysis is performed. The ranking is based on at least one of a historical usability, a historical cost, and a historical usability-to-cost ratio of a camera. A corresponding camera monitoring system, video processing and/or management system, computer program and computer program product are also provided.

Abstract: A method for dewarping a region of a fisheye view image , captured by a fisheye lens wherein the fisheye view image comprises a first dewarping pole, DP1. The method defines a region of interest, ROI, determining a center, G, of the ROI, defining a temporary annulus sector region such that the temporary annulus sector region comprises the ROI, and has its center at DP1 and has a temporary outer arc shaped edge and a temporary inner arc shaped edge, defining a second dewarping pole, DP2 at a distance, D, from DP1 along a radial direction extending from G of the ROI through the DP1, setting a dewarping annulus sector region comprising the ROI. The dewarping annulus sector region has its center at DP2 and has its dewarping inner arc shaped edge maintained at a same radial distance from DP1 as the temporary inner arc shaped edge.

Abstract: A device and method merge a first candidate area relating to a candidate feature in a first image and a second candidate area relating to a candidate feature in a second image. The first and second images have an overlapping region, and at least a portion of the first and second candidate areas are located in the overlapping region. An image overlap size is determined indicating a size of the overlapping region of the first and second images, and a candidate area overlap ratio is determined indicating a ratio of overlap between the first and second candidate areas. A merging threshold is then determined based on the image overlap size, and, on condition that the candidate area overlap ratio is larger than the merging threshold, the first candidate area and the second candidate area are merged, thereby forming a merged candidate area.

Abstract: A method, system, and apparatus related to the field of background subtraction in images is disclosed. In particular, the method, system, and apparatus is related to determining whether pixel positions in an image frame of a video sequence belongs to a background or a foreground of a captured scene using a determined level of dynamics of the pixel position.

Abstract: Methods and apparatus, including computer program products, implementing and using techniques for configuring an artificial neural network to a particular surveillance situation. A number of object classes characteristic for the surveillance situation are selected. The object classes form a subset of the total number of object classes for which the artificial neural network is trained. A database is accessed that includes activation frequency values for the neurons within the artificial neural network. The activation frequency values are a function of the object class. Those neurons having activation frequency values lower than a threshold value for the subset of selected object classes are removed from the artificial neural network.

Abstract: A method, system, and apparatus related to the field of background subtraction in images is disclosed. In particular, the method, system, and apparatus is related to determining whether pixel positions in an image frame of a video sequence belongs to a background or a foreground of a captured scene using a determined level of dynamics of the pixel position.

Abstract: Methods and apparatus, including computer program products, implementing and using techniques for configuring an artificial neural network to a particular surveillance situation. A number of object classes characteristic for the surveillance situation are selected. The object classes form a subset of the total number of object classes for which the artificial neural network is trained. A database is accessed that includes activation frequency values for the neurons within the artificial neural network. The activation frequency values are a function of the object class. Those neurons having activation frequency values lower than a threshold value for the subset of selected object classes are removed from the artificial neural network.