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.

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.

A system and method of inspecting a static monitoring installation, installed in a traffic space. An evaluation circuit is able to create an image of the environment from a signal reflected from an object, wherein at least one reference value of a reference image of the environment is stored in the evaluation circuit, and the at least one reference value is formed from the reflected signals of at least one reference point for a reflected signal

A system and method of inspecting a static monitoring installation, installed in a traffic space. An evaluation circuit is able to create an image of the environment from a signal reflected from an object, wherein at least one reference value of a reference image of the environment is stored in the evaluation circuit, and the at least one reference value is formed from the reflected signals of at least one reference point for a reflected signal

Systems and methods for tracking objects though a traffic control system include a plurality of sensors configured to capture data associated with a traffic location, and a logic device configured to detect one or more objects in the captured data, determine an object location within the captured data, transform each object location to world coordinates associated with one of the plurality of sensors; and track each object location using the world coordinates using prediction and occlusion-based processes. The plurality of sensors may include a visual image sensor, a thermal image sensor, a radar sensor, and/or another sensor. An object localization process includes a trained deep learning process configured to receive captured data from one of the sensors and determine a bounding box surrounding the detected object and output a classification of the detected object. The tracked objects are further transformed to three-dimensional objects in the world coordinates.

Systems and methods for tracking objects though a traffic control system include a plurality of sensors configured to capture data associated with a traffic location, and a logic device configured to detect one or more objects in the captured data, determine an object location within the captured data, transform each object location to world coordinates associated with one of the plurality of sensors; and track each object location using the world coordinates using prediction and occlusion-based processes. The plurality of sensors may include a visual image sensor, a thermal image sensor, a radar sensor, and/or another sensor. An object localization process includes a trained deep learning process configured to receive captured data from one of the sensors and determine a bounding box surrounding the detected object and output a classification of the detected object. The tracked objects are further transformed to three-dimensional objects in the world coordinates.

To reliably detect an object such as a bicycle at increased range, an Advanced Driving Support System uses a deep neural network(s) to process an ambient (grey-scale) image into an object that is then tracked by a second range detection camera. Most objects of interest, such as bicycles and automobiles, are outfitted with one or more retroreflectors that are used to cue the neural network to the object of most interest. As the retroreflectors also tend to saturate the range detection camera, a method is used to manage the saturation and estimate the correct range to the object.

To reliably detect an object such as a bicycle at increased range, an Advanced Driving Support System uses a deep neural network(s) to process an ambient (grey-scale) image into an object that is then tracked by a second range detection camera. Most objects of interest, such as bicycles and automobiles, are outfitted with one or more retroreflectors that are used to cue the neural network to the object of most interest. As the retroreflectors also tend to saturate the range detection camera, a method is used to manage the saturation and estimate the correct range to the object.

A tolling system is operable to reduce the number of manual reviews of a toll point images needed to process toll fee charges by separately reporting from both toll points and mobile device in vehicles running a tolling application program the lane and crossing time when traversing a toll point. A tolling service can match records produced by the toll points with records providing by the mobile device when the toll point cannot immediately determine the identity of the toll customer passing through the toll point.

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.