Systems and corresponding methods are provided for moving object predictive locating, reporting, and alerting. An exemplary method includes receiving moving object data corresponding to a moving object; receiving sensor data from a sensor; merging the received moving object data and received sensor data into a set of merged data; and based thereon, automatically determining one or more of: a predicted location or range of locations for the moving object, a potential path of travel or area for the moving object, and a potential for interaction between the moving object and subject objects. The method can include automatically generating and providing alerts based on the determining. Alert can be configured for users having potential for interaction with the moving object. A method may include receiving sensor data from third parties, and provide information generated by the system pertaining to moving objects to other third parties.

A system, method, and computer program product provides a way to separate connected or adhered adjacent characters of a digital image for license plate recognition. As a threshold processing, the method performs a recognition of character adhesion by obtaining character parameters using an image processor. The parameters include a horizontal max crossing and a ratio of width and height. A first rule-based module is used responsive to the character parameters to distinguish the adhered characters (character adhesions) that are easy to judge, leaving the uncertain part to a character adhesion classifier model for discrimination. Character adhesion data is obtained by data augmentation including the adding of a random distance between two single characters to create class like adhered characters. Then the character adhesion classifier model of single character and character adhesion data is trained. Any uncertain part can be distinguished by the trained character adhesion classifier model.

A method of generating a context-rich parking event of a target vehicle taken by a patrol vehicle including obtaining a plate read event identifying an identifier of the target vehicle; initiating a collection of a first context image of a first view of the target vehicle; obtaining of geolocation information; obtaining temporal information; verifying if at least one condition is met by calculating if at least one of: a temporal constraint threshold is reached by using the temporal information; and a position constraint threshold is reached by using the geolocation information; initiating a collection by the patrol vehicle of a second context image of a second view of the target vehicle; and causing an association between the second context image and the plate read event to generate the context-rich parking event.

The present disclosure relates to identifying vehicles using wireless device identifiers. In accordance with aspects of the present disclosure, a system for identifying vehicles in a parking structure includes a camera configured to capture images of an area in the parking structure where the camera captures an image containing one or more vehicle(s), a detector configured to scan for wireless device identifiers in the area, and a processing system including an electronic storage. The processing system is configured to execute instructions to access the image containing the vehicle(s) in the area and access one or more wireless device identifier(s) scanned by the detector in the area, process the image containing the vehicle(s) to extract one or more license plate identifier(s) corresponding to the vehicle(s), and store in the electronic storage at least one record associating the license plate identifier(s) with the wireless device identifier(s).

Embodiments relate to systems, methods and computer readable media for parking monitoring in an urban area by image processing operations. Embodiments perform parking monitoring by capturing images of an urban area, comparing captured images with reference images to determine location and parking conditions. Embodiments processes captured images to detect licence plates, vehicles or parking signs to determine compliance of vehicles with parking conditions.

Methods, systems, and apparatus, including medium-encoded computer program products, for a secure edge platform that uses image classification machine learning models. An edge platform can include at least one camera and can identify image classification models that generate classification output data from image data generated by the cameras. The edge platform can receive image data generated by the camera, and provide the image data to the models. In response to providing the image data classification models, the edge platform can receive classification output data. In response to receiving the classification output data from the image classification models, the edge platform can generate augmentation data that is associated with the image data, then transmit detection data to a central server platform. The detection data can include (i) the classification output data and (ii) the augmentation data associated with the image data. Data can be made recordable, reportable, searchable, and alarmable.

A recording control apparatus includes: a captured data acquisition unit configured to acquire captured data from an image capturing apparatus; an event detection unit configured to detect an occurrence of an event; a captured data analysis unit configured to analyze the captured data and thereby detect a predetermined object included in the captured data; and a recording control unit configured to store, as event recording data, the captured data including a video image at a point in time when the event has occurred and the captured data including a video image at a point in time when the object has been detected.

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 system for monitoring vehicle traffic may include a camera positioned to capture images within a license plate detection zone, the images may represent license plates of vehicles. The system may include an electronic device identification sensor that detects and stores electronic device identifiers of electronic devices located within an electronic device detection zone, and a computing system that detects, using the images, a license plate ID of a vehicle, compare the license plate ID of the vehicle to a database of trusted vehicle license plate IDs, identifies the vehicle as a suspicious vehicle, the identification based at least in part on the comparison of the license plate ID of the vehicle to the database of trusted vehicle license plate IDs, and correlates the license plate ID of the vehicle with at least one of the plurality of stored electronic device identifiers.

Provided is an Aobject-based short range measurement method, a short range measurement device, a short range measurement system, and a storage medium. The short range measurement method includes: identifying a target object, and acquiring border information about an ROI of the target object; acquiring two groups of geometric constraint points of the target object with respect to a left-eye camera and a right-eye camera respectively; acquiring pixel coordinates of each geometric constraint point and a border pixel size corresponding to the border information, and calculating a monocular distance estimation value of the target object; acquiring an overall disparity of the two groups of geometric constraint points, and calculating a binocular distance estimation value of the target object in accordance with the overall disparity; and acquiring a final measurement value in accordance with the monocular distance estimation value and the binocular distance estimation value.