Axis Ab

Hanna Björgvinsdóttir

Jiandan Chen

Ludvig Pettersson

Markus Skans

Simon Molin

Jakob Grundström

Martin Ljungqvist

Joakim Baltsén

Amanda Tydén

Robin Seibold

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.

Postprocessing prioritization

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, DP. 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 DPand has a temporary outer arc shaped edge and a temporary inner arc shaped edge, defining a second dewarping pole, DPat a distance, D, from DPalong a radial direction extending from G of the ROI through the DP, setting a dewarping annulus sector region comprising the ROI. The dewarping annulus sector region has its center at DPand has its dewarping inner arc shaped edge maintained at a same radial distance from DPas the temporary inner arc shaped edge.

Method and device for dewarping a region of a fisheye view image

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.

Device and a method for merging candidate areas

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.

Method, device and system for determining whether pixel positions in an image frame belong to a background or a foreground

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.

Artificial neural network class-based pruning

Methods and apparatus, including computer program products, implementing and using techniques for classifying an object occurring in a sequence of images. The object is tracked through the sequence of images. A set of temporally distributed image crops including the object is generated from the sequence of images. The set of image crops is fed to an artificial neural network trained for classifying an object. The artificial network determines a classification result for each image crop. A quality measure of each classification result is determined based on one or more of: a confidence measure of a classification vector output from the artificial neural network, and a resolution of the image crop. The classification result for each image crop is weighed by its quality measure, and an object class for the object is determined by combining the weighted output from the artificial neural network for the set of images.

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Lund, Sweden

Hanna Björgvinsdóttir