Automated processing, enforcement and intelligent management of vehicle operation violations is disclosed. A method, that is also disclosed, includes obtaining at least one image within which is shown at least a portion of a vehicle. The method also includes receiving image data for the at least one image, and analyzing the image data to generate a violation score for each of one or more potential vehicle operation violations for the vehicle. When the violation score is in-between an upper violation threshold and a lower consideration threshold, user input is obtained that either affirms or rejects existence of a violation and then, once the user input is obtained, a violation notification is generated only when the user input affirms the existence of the violation. When the violation score is higher than the violation threshold, the violation notification is generated without the user input being obtained.

Disclosed is a method for a network to communicate with a vehicle in a wireless communication system. The method may comprise: receiving a location message from a vehicle stopped in a predetermined area; receiving a message about a traffic light system from a traffic light in the predetermined area; and acquiring the vehicle registration number of the stopped vehicle on the basis of a predetermined condition being satisfied. In particular, the predetermined condition may be satisfied when the stopped vehicle is determined to have committed a parking violation on the basis of the location message of the stopped vehicle and the message about the traffic light system.

According to an embodiment of the present invention, a multi-resolution image processing device capable of recognition of a plurality of dynamic objects comprises a dynamic object identifier extracting a motion vector from an input image and identifying a dynamic object; a dynamic object ID imparter assigning an ID to the dynamic object identified by the dynamic object identifier; a dynamic object artificial intelligence determiner determining an authenticity of the dynamic object identified by the dynamic object identifier based on a standard shape for each type of dynamic object learned and stored, classifying at least one object of interest and object of no interest designated by a user, and removing the object of no interest; a region-of-interest detector extracting a feature point from the at least one object of interest and setting at least a portion of the object of interest as a region of interest; and a variable compressor compressing the image with different compression rates for the region of interest and the object of interest.

A sensor device includes an array sensor having a plurality of detection elements arrayed in one or two dimensional manner, a signal processing unit configured to acquire a detection signal by the array sensor and perform signal processing, and a calculation unit. The calculation unit detects an object from the detection signal by the array sensor, and gives an instruction, to the signal processing unit, on region information generated on the basis of the detection of the object as region information regarding the acquisition of the detection signal from the array sensor or the signal processing for the detection signal.

Disclosed are devices, systems and methods for managing parking monitoring and enforcement. Parking at a meter or in a parking lot or ramp can be provided as a subscription. A registration and verification process for the subscription service can be provided as a multi-step process that combines the smart parking meter with a QR code, a mobile parking app for a smartphone, a central parking management system, and digital images of the parked vehicle(s). The registration process verifies that only vehicles owned by the subscriber are associated with the subscriber's account. Steps can also be employed to ensure that subscribers are not incorrectly issued parking citations by determining whether license plate data for a vehicle initially determined to be in violation matches an active subscription parker's roster of registered vehicles.

System and methods are disclosed for capturing license plate (LP) information of a vehicle in relative motion to a camera device. In one example, the camera system detects the LP in multiple frames, then aligns and geometrically rectifies the image of the LP by scaling, warping, rotating, and/or performing other functions on the images. The camera system may optimize capturing of the LP information by executing a temporal noise filter on the aligned, geometrically rectified images to generate a composite image of the LP for optical character recognition. In some examples, the camera device may include an image sensor, such as a high dynamic range (HDR) sensor, modified to set long and short exposures of the HDR sensor to capture frames of a vehicle's LP, but without consolidating the images into a composite image. The camera system may set optimal exposure settings based on detected relative speed of the vehicle.

Implementations generally relate to estimating grouped observations. In some implementations, a method includes detecting a license plate of a vehicle. The method further includes capturing a plurality of images of the license plate. The method further includes computing candidate regions associated with the license plate based on the images of the license plate. The method further includes computing candidate characters of the license plate based on the candidate regions and the images of the license plate. The method further includes selecting target characters from the candidate characters based on one or more predetermined selection criteria.

A parking guidance system of a parking lot includes: a first license plate number recognizing device recognizing a license plate number of a vehicle entering or exiting from a parking lot; and a server classifying a priority of the vehicle and distinguishing whether the vehicle is a parking or charging use vehicle based on the license plate number, assigning a parking or charging spot based on at least one of the classified priority, information on whether the vehicle is the parking use vehicle or the charging use vehicle, or information on remaining parking spots and remaining charging spots, providing a guidance on a position of the assigned parking spot or charging spot to the vehicle until the vehicle arrives at the position, and determining a parking fee or a charging fee of the vehicle and providing a guidance on the fee when the vehicle exits the parking lot.

In some aspects, an edge appliance is placed in an active mode and causes a software agent that is based on a machine learning algorithm to engage in a conversation to take an order from a customer that is located at an order post. The edge appliance provides, using a communication interface, audio data that includes the conversation, to a communications system of a restaurant. The edge appliance provides, using the communication interface, a content of a cart associated with the order to a point-of-sale terminal of the restaurant. If the edge appliance determines, using the communication interface, that a microphone of the communication system is receiving audio input from an employee, the edge appliance automatically transitions the edge appliance from the active mode to an override mode, enabling the employee to receive a remainder of the order from the customer.

A method includes obtaining, using at least one processing device, a vanishing point and a boundary based on image data associated with a scene, where the boundary is associated with a detected object within the scene. The method also includes repeatedly, during multiple iterations and using the at least one processing device, (i) identifying multiple patches within the boundary and (ii) determining a similarity of the image data contained within the multiple patches. The method further includes identifying, using the at least one processing device, a modification to be applied to the boundary based on the identified patches and the determined similarities. In addition, the method includes generating, using the at least one processing device, a refined boundary based on the modification, where the refined boundary identifies a specified portion of the detected object.