A system, method and program storage device are provided for automatically associating evidence recorded by a plurality of cameras with a discrete occurrence, including: receiving occurrence data pertaining to the discrete occurrence and storing at least a portion of the occurrence data in an occurrence record; receiving first evidence data comprising at least a video data portion and a metadata portion of the evidence recorded by a first camera of the plurality of cameras and storing it in an evidence record; receiving second evidence data comprising at least a video data portion and a metadata portion of the evidence recorded by a second camera of the plurality of cameras and storing it in the evidence record; automatically associating information stored in the evidence record with information stored in the occurrence record based on a correspondence of at least two criteria including a first criterion of time; identifying, based on the automatic association, a first image data portion of the evidence recorded by the first camera that is related to the discrete occurrence while excluding a second image data portion of the evidence recorded by the first camera that is unrelated to the discrete occurrence; and identifying, based on the automatic association, a third image data portion of the evidence recorded by the second camera that is related to the discrete occurrence while excluding a fourth image data portion of the evidence recorded by the second camera that is unrelated to the discrete occurrence.

A target identification method includes receiving target detection information for a current target from a device, and acquiring target state information of multiple reference targets. A degree of matching between the target detection information and the target state information of each of the multiple reference targets is determined; and the current target is associated with a corresponding reference target having a best degree of matching amongst the multiple reference targets.

A method for generating learning data for the neural network may comprise generating a license plate image by combining a background image, a frame image and a text image, generating a transformed image by performing at least one of a geometry transformation and a filter transformation on the license plate image, setting a text corresponding to the text image as target data for the transformed image, and generating the learning data including the transformed image and the target data.

Embodiments of the present disclosure provide a method, a computer program product, and an arrangement (200) for object location information provisioning for autonomous vehicle (100) maneuvering. The method comprises receiving (S21) a request for object location information from at least one autonomous vehicle (100). The method comprises retrieving (S23) vulnerable road user, VRU, data, from a plurality of VRU data sources (104a-104n), wherein the VRU data comprises respective VRU locations in a pre-determined surrounding of the autonomous vehicle (100). Further, the method comprises determining (S25) the object location information based on the retrieved VRU data. Additionally, the method comprises periodically (S27) transmitting the determined object location information to the autonomous vehicle (100).

A vehicle control system for providing information to support an overtaking maneuver by a first vehicle, the vehicle control system includes: a processing circuitry adapted to be operatively connected to a traffic view camera and configured to: receive image data of at least one other vehicle in traffic by the traffic view camera; determine, based on the image data of the at least one other vehicle in traffic, overtaking support data indicative of at least one gap in the traffic suitable for an overtaking maneuver; and provide the overtaking support data to the first vehicle for supporting an overtaking maneuver by the first vehicle.

A vehicle control system for providing information to support an overtaking maneuver by a first vehicle, the vehicle control system includes: a processing circuitry adapted to be operatively connected to a traffic view camera and configured to: receive image data of at least one other vehicle in traffic by the traffic view camera; determine, based on the image data of the at least one other vehicle in traffic, overtaking support data indicative of at least one gap in the traffic suitable for an overtaking maneuver; and provide the overtaking support data to the first vehicle for supporting an overtaking maneuver by the first vehicle.

The present invention relates to a method for monitoring a harbor performed by a computing device, the method for monitoring the harbor according to an aspect of the present invention comprising: obtaining a harbor image having a first view attribute; generating a segmentation image having the first view attribute and corresponding to the harbor image by performing an image segmentation using an artificial neural network trained to output information, from an input image, related to an object included in the input image; generating a transformed segmentation image having a second view attribute from the segmentation image having the first view attribute based on a first view transformation information used to transform an image having the first view attribute into an image having the second view attribute different from the first view attribute; and calculating berthing guide information of the ship based on the transformed segmentation image.

The present invention relates to a method for monitoring a harbor performed by a computing device, the method for monitoring the harbor according to an aspect of the present invention comprising: obtaining a harbor image having a first view attribute; generating a segmentation image having the first view attribute and corresponding to the harbor image by performing an image segmentation using an artificial neural network trained to output information, from an input image, related to an object included in the input image; generating a transformed segmentation image having a second view attribute from the segmentation image having the first view attribute based on a first view transformation information used to transform an image having the first view attribute into an image having the second view attribute different from the first view attribute; and calculating berthing guide information of the ship based on the transformed segmentation image.

A method for detecting an infringement by vehicle operator is described. The method comprises detecting a vehicle; receiving one or more image of at least a part of the vehicle operator; automatically analysing with a neural network the one or more captured received image to detect an infringing act; and providing the one or more captured received images comprising the detected infringing act to thereby detect the infringement. Also described are a system, a device, a computer system and a computer program product all for detecting an infringement by a vehicle operator. The device may comprise one or more flash for illuminating the vehicle or a part thereof with light at a narrow band and one or more camera comprising a narrow band filter that lets through only the wavelengths of light produced by the one or more flash.

A method for detecting an infringement by vehicle operator is described. The method comprises detecting a vehicle; receiving one or more image of at least a part of the vehicle operator; automatically analysing with a neural network the one or more captured received image to detect an infringing act; and providing the one or more captured received images comprising the detected infringing act to thereby detect the infringement. Also described are a system, a device, a computer system and a computer program product all for detecting an infringement by a vehicle operator. The device may comprise one or more flash for illuminating the vehicle or a part thereof with light at a narrow band and one or more camera comprising a narrow band filter that lets through only the wavelengths of light produced by the one or more flash.