A system and method to manage a parking area. The system may include one or more tags; a license plate reader configured to read license plates; and a tag reader configured to, in response to a license plate read by the license plate reader, read the tag and determine whether the read license plate belongs to an authorized or unauthorized vehicle.

A method and an apparatus for pushing vehicle information, a user account server, and user equipment are provided. The method includes: receiving an entry event that is of a vehicle and that is sent by a parking management server, and obtaining geo-fence information of a parking lot managed by the parking management server; obtaining, by a user account server, a user account bound to the license plate number of the vehicle; obtaining a location determining result of user equipment corresponding to the user account; and pushing the entry event of the vehicle to the user equipment when the current location of the user equipment is within a geo-fence range.

Methods and systems for determining at least one candidate user for a license plate number (LPN) are described herein. A set of license plate recognition (LPR) events that correspond to the LPN and a set of access events of a plurality of users may be obtained. One or more associated events for each respective user of the plurality of users may be determined. A confidence score for each respective user may be determined based on the one or more associated events for the respective user. At least one candidate user for the LPN may be identified based on the confidence score. An indication that the at least one candidate user is a candidate for the LPN is output. Methods and systems for determining at least one candidate license plate number for a user are also described herein. Additional related methods and systems are described herein.

Systems and methods for monitoring a throughway using a radio frequency identification (RFID) detection system. The RFID detection system includes (i) an image sensor configured to have a field of view directed towards a lane of the throughway; (ii) an RFID transceiver arrangement configured to interrogate RFID tags disposed on vehicles within the lane of the throughway; and (iv) a controller operatively connected to the image sensor and the RFID transceiver arrangement. The controller is configured to (1) cause the image sensor to capture a frame of image data representative of the lane of the throughway; (2) analyze the frame of image data to detect a presence of a vehicle in the lane of the throughway; (3) based on the analysis, determine a position of the vehicle relative to the RFID transceiver arrangement; and (4) configure an antenna array to generate a beam directed at the position of the vehicle.

In some embodiments, apparatuses and methods are provided herein useful to train a machine learning algorithm to detect text of interest. In some embodiments, there is provided a system to detect vertically oriented text of interest including a first data set comprising a plurality of captured digital images each depicting an object of interest and a second data set comprising a plurality of augmented digital images each depicting a captured digital image augmented with a synthetic text image; a first control circuit configured to cause the machine learning algorithm to output a machine learning model trained to automatically detect occurrences of vertically oriented text of interest based on the first data set and the second data set; at least one camera; and a second control circuit configured to execute the machine learning model to automatically detect vertically oriented text of interest on the object of interest.

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 vehicle monitoring system includes at least one camera, and a server that is communicably connected to the camera and a client terminal. The camera captures an occupant's face of a vehicle entering an angle of view of the camera, and transmits a captured video to the server. The server acquires an analysis results of an attribute of the vehicle, an analysis result of the occupant's face, and an analysis result of the number of occupants based on the captured video, and accumulates the acquired analysis results as analysis results of the captured video, and sends the analysis result to the client terminal in correlation with a snapshot of the captured video.

A method of operating a carwash includes driving a vehicle to a point of sale station of the carwash, purchasing a carwash transaction, and taking a first picture of a license plate number of the vehicle after the completion of the purchase of the carwash transaction at the point of sale station. The license plate number is associated with the carwash transaction as a carwash ticket. The vehicle is then sent to a carwash queue disposed downstream of the point of sale station. The carwash ticket information is sent to a carwash tunnel. A second picture of the license plate number of the vehicle is taken right as it approaches the carwash tunnel. The license plate number is associated with the carwash ticket and the vehicle is washed according to a wash dictated by the carwash ticket.

In some aspects, a computing device receives a scan of a code displayed on an order post located near a restaurant, determines that the code is associated with the restaurant, and automatically opens a software application and navigates the software application to an ordering page associated with the restaurant. The computing device initiates receiving, via the software application, input associated with an order, sends the input to a machine learning based software agent executing on a server, receives a predicted response to the input, provides the predicted response as audio output and/or displays the predicted response on the touchscreen display device. After the order is complete, the computing device sends order data associated with the order to the restaurant. After receiving an indication from the restaurant that the order is ready, the computing device indicates that the order is ready to be picked up.

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.