29 results about "Vehicle tracking system" patented technology

An RFID transponder including a Bluetooth® compatible transceiver (BLEET) is described. The Bluetooth® compatible transceiver may be configured to set data that is transmitted via one or more RFID transceivers in the transponder and to return data received by the RFID transceiver(s) to a client application running, for example, on a user's smart phone. The BLEET may be used for electronic vehicle tracking or tolling. Vehicle occupancy data may be set by the user with the client application via a Bluetooth® connection in connection with high occupancy vehicle tolling and express lane incentive programs. The BLEET may be used in a congestion pricing (CP) toll area, wherein the smart phone determines a toll rate based on the location, when triggered by the BLEET, that the BLEET was read by an RFID reader located on a boundary of the CP toll area.

The invention relates to the technical field of image recognition, in particular to an unmanned aerial vehicle tracking system and method based on image recognition, and the system comprises a spectrum optimization module, a trajectory analysis module, a form adjustment module, a flight anomaly detection module and a tracking optimization module. According to the method, the brightness information of the ambient light and the reflected light of the unmanned aerial vehicle is finely analyzed and the spectral response of the image is adjusted, so that the image definition is greatly improved, the unmanned aerial vehicle can be accurately identified under various illumination conditions, and the flight path of the unmanned aerial vehicle is calculated in real time by accurately analyzing the image bounding box and timestamp information of the unmanned aerial vehicle. According to the method and the system, the tracking process is ensured to be continuous and accurate, any form change is effectively captured by continuously monitoring the form of the unmanned aerial vehicle in continuous image frames, the behavior mode change of the unmanned aerial vehicle is responded in time, and in the aspect of flight abnormity monitoring, behaviors inconsistent with a conventional flight mode can be accurately analyzed, and potential security threats can be identified in time.

The invention provides an unmanned aerial vehicle tracking system and method based on state space modeling and scale searching, and belongs to the technical field of artificial intelligence. The invention aims to solve the problems of low recognition rate and high false alarm rate when a traditional unmanned aerial vehicle tracking method is used for processing low-altitude complex backgrounds, illumination changes and multi-shielding scenes. The tracking system comprises a target detection module, a target tracking module and a dual-threshold closed-loop control module. The tracking method comprises the following steps: performing feature extraction and candidate target identification on an input video stream or image sequence through a target detection module, and outputting a detection result in the form of a coordinate frame and confidence; the target tracking module performs continuous frame association and real-time position updating on candidate unmanned aerial vehicle targets through a multi-scale twin convolutional network; and the double-threshold closed-loop control module receives an output result, automatically judges a current task state according to a detection confidence threshold and a tracking frame number threshold, and intelligently switches a tracking mode and a detection mode.

The invention discloses a self-adaptive vehicle navigation filtering method and device, and belongs to the technical field of intelligent driving. The method comprises the following steps: constructing a nonlinear uncertainty tracking system model according to the mutability of an actual vehicle tracking state, the nonlinearity of a sensor measurement equation and the non-Gaussian property of a measurement error; aiming at a nonlinear uncertainty tracking system model, combining strong tracking filtering and a maximum entropy criterion, and constructing a cost function for estimating the optimal state of the unmanned vehicle; determining a fading factor in the cost function based on the orthogonality of the measurement residual sequence; constructing a novel adaptive navigation filtering algorithm by combining an unscented Kalman filtering framework according to the cost function and the fading factor; and carrying out data processing on the unmanned vehicle tracking system according to the constructed adaptive navigation filtering algorithm. According to the method, the problems of mutability, nonlinearity, non-Gaussian property and the like in a nonlinear tracking system can be inhibited at the same time, and the tracking precision and reliability of the unmanned vehicle in a complex environment are improved.

A multi-vehicle tracking system and method based on inter-frame similarity for roadside lidar includes: an encoder unit, a decoder unit, a regulator unit, an appearance parameter processing unit, a kinematic parameter processing unit, a point cloud cluster similarity calculation unit, and a global trajectory information optimization unit. This invention achieves accurate tracking of multiple target vehicles within the perception range of roadside lidar (including high-mobility conditions) based on an improved Type I Siamese neural network. By selecting appropriate vehicle static parameters, kinematic parameters, and loss weights, the algorithm can effectively utilize the appearance and kinematic features of the target to achieve tracking under high-mobility driving conditions of roadside vehicles, effectively solving the problem of high-mobility vehicle driving in roadside perception scenarios.

An RFID transponder including a Bluetooth® compatible transceiver (BLEET) is described. The Bluetooth® compatible transceiver may be configured to set data that is transmitted via one or more RFID transceivers in the transponder and to return data received by the RFID transceiver(s) to a client application running, for example, on a user's smart phone. The BLEET may be used for electronic vehicle tracking or tolling. Vehicle occupancy data may be set by the user with the client application via a Bluetooth® connection in connection with high occupancy vehicle tolling and express lane incentive programs. The BLEET may be used in a congestion pricing (CP) toll area, wherein the smart phone determines a toll rate based on the location, when triggered by the BLEET, that the BLEET was read by an RFID reader located on a boundary of the CP toll area.

The utility model relates to unmanned aerial vehicle load installation technical field, specifically disclose unmanned aerial vehicle tracking system adopts rotatable multispectral camera mount, include: the both sides of unmanned aerial vehicle shell inside are equipped with U type frame, and the horizontal end surface of frame bottom is set up column, and the spring is sleeved on the column, and the spring top elastic support motor platform, and the top of motor platform is fixed with bolt step motor, and the step motor shaft is connected multispectral camera fixed platform through the shaft coupling, and this fixed platform bottom is installed multispectral camera with screw, and the horizontal end surface of U type frame top is installed linear push rod motor downwards. In the device, multispectral camera is connected coaxially through fixed platform, shaft coupling and step motor, and the step motor can adjust the camera shooting angle; The step motor is placed on the motor platform, and the mechanism formed by the U type frame, the column, the spring and the linear push rod motor is used to adjust the pitch and roll angle of the motor platform, so that the multispectral camera can shoot and track in a larger range.

The application relates to a cross-camera vehicle tracking system and method based on license plate history sequence matching, and relates to a cross-camera vehicle tracking system and method. In order to solve the problem that license plate matching between different cameras is difficult in a large traffic network, especially in the case that a high-flow road section or multiple camera coverage areas overlap, license plate matching and vehicle tracking cannot be quickly and accurately performed, the application performs license plate recognition on each camera-captured video stream, takes all license plate recognition results of the same camera tracking vehicle as a sequence, performs Hungarian matching on license plate recognition history sequences of multiple cameras, and if the history license plate recognition sequences of two vehicles of different cameras can be matched, the two vehicles are considered to be the same tracking target. If the matching is successful and it is confirmed that the two vehicles are the same, the system will assign the same unique ID to the two vehicles, so as to realize continuous tracking across cameras. The application belongs to the technical field of computer vision.

A web-based tracking device system comprising; a housing; a location module that is placed inside said housing; a connecting module that is connected to said location module; an internet module that has a fixed connection with said connecting module; a network internet provider that has a communication with said internet module; a microcomputer that has a link with said connecting module thru said internet module; a wireless module that has a connection with said microcomputer; a shock sensor that gives a signal to said connecting module; an API that builds connection with said network internet provider; an internet access that builds communication with said connection module thru internet; a web / database server that has a connection with said internet access; and an admin and user have access with said web / database server.

Method for determining the current position of a rail vehicle (1) on a rail line (3) travelled by the rail vehicle (1), having the following features: a) detecting the soil structure (5) below the rail line (3) travelled by the rail vehicle (1) below the ground surface by means of a penetrating ground radar (10), b) transmitting the ground data (12) characterising the ground structure (5) recorded by the ground radar (10) by means of remote data transmission from the rail vehicle (1) to a remote evaluation point (2), c) comparing the soil data (12) received at the evaluation station (2) with data records (22) of a soil database (21) in which soil data characterising the soil structure of all rail lines (3) travelled by the rail vehicle (1) are stored, d) determining the current position of the rail vehicle (1) based on the detection of a sufficient degree of correlation of the ground data (12) received by the rail vehicle (1) with at least one data set (22) of the ground database (21) and a position reference of this data set (22) stored in the ground database (21).

An RFID transponder including a Bluetooth® compatible transceiver (BLEET) is described. The Bluetooth® compatible transceiver may be configured to set data that is transmitted via one or more RFID transceivers in the transponder and to return data received by the RFID transceiver(s) to a client application running, for example, on a user's smart phone. The BLEET may be used for electronic vehicle tracking or tolling. Vehicle occupancy data may be set by the user with the client application via a Bluetooth® connection in connection with high occupancy vehicle tolling and express lane incentive programs. The BLEET may be used in a congestion pricing (CP) toll area, wherein the smart phone determines a toll rate based on the location, when triggered by the BLEET, that the BLEET was read by an RFID reader located on a boundary of the CP toll area.

A system combining readers for RFID transponders and Bluetooth® devices for automated roadway tolling and monitoring is described. Because of the ubiquity of Bluetooth® devices, filtering methods are described to narrow received Bluetooth® signals to a particular monitored traffic lane and to further associate the Bluetooth® signal with a registered RFID toll tag in a vehicle. Signal strength of the Bluetooth® signal at each of a plurality of receiver antennas may be used to identify the location of the Bluetooth® device that emitted the signal.

The application discloses a kind of self-adapting vehicle navigation filtering method and device, belong to intelligent driving technical field.It includes: according to the abruptness of actual vehicle tracking state, the nonlinearity of sensor measurement equation and the non-Gaussian of measurement error, construct nonlinear uncertainty tracking system model;For nonlinear uncertainty tracking system model, combined with strong tracking filter and maximum entropy criterion, construct the cost function of unmanned vehicle optimal state estimation;Based on the orthogonality of measurement residual sequence, determine the fading factor in cost function;According to cost function and fading factor, combined with unscented kalman filter framework, construct new self-adapting navigation filtering algorithm;According to the data processing of unmanned vehicle tracking system of the self-adapting navigation filtering algorithm constructed.This application can simultaneously suppress the abruptness, nonlinearity and non-Gaussian in nonlinear tracking system and other problems, improve the tracking precision and reliability of unmanned vehicle in complex environment.

To provide a vehicle tracking method, a vehicle tracking system, and a program.SOLUTION: A vehicle tracking method includes: receiving dispersion type optical fiber sensing (DFOS) data; identifying hit points in the DFOS data, each hit point corresponding to a position of a corresponding vehicle at a detection time; clustering the identified hit points to define one or more clusters; classifying each of the one or more clusters into a first classification or a second classification based on a quantity of hit points and a quantity of detection times in each of the one or more clusters; and estimating a vehicle parameter of a first vehicle corresponding to the hit point of the first cluster of one or more clusters based on the hit point of the first cluster having the first classification.SELECTED DRAWING: Figure 2

According to the vehicle auxiliary driving moving target real-time detection and tracking method, convolution filtering is established, a minimum output deviation and square and weak deviation filtering method is established, and the method solves the problem that in the tracking process, the scale of a vehicle changes at any time, the target is difficult to track accurately through the algorithm, and the tracking precision is poor. The method comprises the following steps: firstly, quickly estimating the position of a target on a new frame by using an improved quadratic sum weak deviation function, then calculating a scale evolution factor of the target in the new frame, finally uniformly calculating and updating a filter and the scale evolution factor, and updating the model appearance, the central position and the scale evolution factor of the target on line. And the model scale change is resisted. The improved algorithm can well cope with the scale change of a moving target, meets the requirements of a complex and diversified vehicle auxiliary driving environment for a tracking system, and can meet the requirements of a vehicle-mounted tracking system for the real-time performance, the reliability and the robustness performance of the tracking algorithm.

To provide a vehicle tracking method, a vehicle tracking system, and a program.SOLUTION: A vehicle tracking method includes: receiving dispersion type optical fiber sensing (DFOS) data; specifying a plurality of first hit points inside the DFOS data, each of which corresponds to a position of a corresponding first vehicle at a detection point; determining an initial seed point from among the plurality of specified first hit points; determining whether or not any of the plurality of specified first hit points is an outlier hit point; defining a first cluster by clustering the plurality of specified first hit points from which an arbitrary outlier hit point is excluded; and estimating first vehicle parameters of the first vehicle based on the first cluster.SELECTED DRAWING: Figure 2

1. Name of the product in this design: Graphical User Interface for Intelligent Vehicle Attention System of Electronic Device. 2. Purpose of this design: It is for an electronic device. 3. The key design features of this product are its graphical user interface content. 4. The image or photograph that best illustrates the design's key features: the front view. 5. No design points are specified, therefore other views are omitted. 6. Purpose of the graphical user interface: The main view is the homepage of the intelligent vehicle tracking system; in the main view, dragging the "three driving modes" icon to the middle position will display a diagram showing the interface changes.

The invention discloses an unmanned aerial vehicle tracking system and method based on an intelligent lamp pole network, and the system comprises a sensing layer which is disposed on a plurality of intelligent lamp poles and is used for obtaining sensing data; the network layer is used for transmitting the sensing data; the platform layer is used for receiving the sensing data and carrying out intelligent processing and decision making; wherein the sensing layer, the network layer and the platform layer are connected in sequence; the sensing layer is composed of heterogeneous multi-mode sensing units deployed on a plurality of intelligent lamp poles, and the intelligent lamp poles are deployment carriers of the system. According to the invention, full-process automatic tracking from unmanned aerial vehicle detection and identification to continuous trajectory generation is realized.

The present invention tracks the position and direction of travel of a stolen vehicle.　This vehicle tracking system is provided with a server device comprising: a receiving unit that receives, in chronological order by means of a plurality of receiving devices, an identification ID transmitted from a transmitting device that is installed in a vehicle and that is capable of transmitting the identification ID using two types of radio waves having different communication arrival distances, and that receives, in chronological order from the plurality of receiving devices, reception information of the identification ID by means of the two types of radio waves, together with position information of each reception device; and a vehicle position estimating unit that estimates the position and direction of travel of the vehicle by chronologically plotting reception states of the two types of radio waves on a road map.

The present disclosure generally relates to methods, systems, apparatuses, and non-transitory computer readable media for incident detection using radar sensory data. A vehicular tracking system comprises at least one on-vehicle sensor, such as a radar sensor, that can perceive the environment around the vehicle and capture data related to possible incidents that may be viewed by the sensor. A radar sensor may provide radar data that can be used to calculate velocity vectors, accelerations vectors, azimuth and elevation angles of other vehicles, and this data may be collected and stored for possible incident characterization and accident investigation. The vehicle with the sensor may be configured to behave as a third-party witness to possible incidents. Several sensors may be used in conjunction with one another, where one sensor may trigger another sensor to begin capturing other data that the first sensor may be unable to capture.

The present invention tracks the location of a stolen vehicle.　This vehicle tracking system comprises a server provided with: a reception unit that receives, using a plurality of reception devices, an identification ID that is transmitted by a transmission device, which is mounted on a stolen vehicle and transmits two types of radio waves having different communication ranges, and that receives, from the plurality of reception devices, location information of each reception device together with reception information of the identification ID using the two types of radio waves; and a vehicle location estimation unit that estimates the location of the vehicle by plotting the reception states of the two types of radio waves on a road map.

The utility model discloses a positioning accessory and a recreational vehicle tracking system, the positioning accessory is applied to the recreational vehicle tracking system, the recreational vehicle tracking system comprises a recreational vehicle body and a head display device, the head display device is provided with a camera module, the positioning accessory comprises a base and a positioning module, the base is used for being installed on the recreational vehicle body, and the positioning module is arranged on the base. The camera module is arranged on the base, the positioning module is arranged on the base, the positioning module is provided with a light identification assembly, the light identification assembly is used for outputting a light identification pattern to be acquired by the camera module, and the design assists in azimuth correction of the amusement vehicle, so that the tracking accuracy of the amusement vehicle is improved, and rendering data can be conveniently output to the head display equipment.

A system combining readers for various communication protocols (e.g., RFID transponders and Bluetooth devices) for automated roadway tolling and monitoring is described. Because of the ubiquity of communication devices (e.g., Bluetooth devices), filtering methods are described to narrow received signals (e.g., Bluetooth signals) to a particular monitored traffic lane and to further associate the signal (e.g, Bluetooth signal) with a registered toll tag (e.g., RFID toll tag) associated with a vehicle. The signal strength of the signal (e.g, Bluetooth signal) at each of a plurality of receiver antennas may be used to identify the location of the communication device (e.g., Bluetooth device) that emitted the signal.

Disclosed are systems and methods for supporting safe vehicle use. Specifically, machine learning is applied to identify and accumulate data from electronic services. The machine learning is further used to identify risk associated with a potential use of a vehicle. The identified risk is a basis for safety customizations of the vehicle, based upon capabilities of the vehicle.

A system for tracking vehicle position using a smartphone in the vehicle as an active transponder, which is detected by roadside equipment, is described. The system may use existing RF transceivers on the smartphone, such as BLE or WiFi, to periodically transmit an identifying message. Road-based equipment detects and locates the smartphone. The smartphone may be alerted and / or awaken by roadside beacons, and then respond with its identification information. Processing can be performed on the smartphone and / or by roadside or central office equipment. Vehicle location detection can be enhanced through the use of directional antenna matrices, such as a Butler matrix. The system can be used for automated roadway tolling and monitoring.

The invention relates to the technical field of intelligent traffic, in particular to a vehicle cross-boundary tracking method and device, electronic equipment and a storage medium, the method is applied to a vehicle tracking system, and the vehicle tracking system comprises a management platform and an algorithm platform which are connected in sequence; the method comprises the following steps: through a configuration rule line of a management platform, establishing a logic pairing relationship between an entry rule line and an exit rule line, so as to generate an algorithm task containing a camera ID, a rule line coordinate and a region pairing relationship; and the algorithm platform receives the algorithm task, performs vehicle cross-border tracking based on configuration data of the algorithm task, and feeds back a tracking result to the management platform. The monitoring rule is configured based on the management platform, the monitoring platform is controlled to perform vehicle cross-border tracking based on the monitoring rule under the action of the task platform, vehicle cross-border tracking in a complex scene can be adapted, the track integrity is improved, and the error rate is reduced.

The application provides a vehicle tracking method and system based on multi-camera cooperation. The application utilizes the cooperation among cameras, identifies feature points such as the time, geographic coordinates, vehicle features and motion direction of vehicles passing through different cameras, automatically generates the vehicle trajectory among the cameras, and draws it on a map to form a vehicle tracking system based on camera cooperation.

A system for tracking vehicle position using a smart phone in the vehicle as an active transponder, which is detected by roadside equipment is disclosed. In an embodiment, the system uses existing RF transceivers on the smart-phone, such as Bluetooth LE or WiFi to periodically transmit an identifying message. Road-based equipment detects and locates the smart phone. In a further embodiment, the smart phone is alerted by roadside beacons and only then responds with its identification information. Processing can be performed either on the smart phone or by roadside or central office equipment as is the case in prior art active or passive transponder-based tolling systems. Vehicle location detection can be enhanced through the use of directional antenna matrices such as a Butler matrix. The system can be used for automated roadway tolling and monitoring.

A system is described for tracking vehicle position using a smart phone or similar device as an active transponder that communicates with roadside equipment. The system may use existing RF transceivers on the smart-phone, such as Bluetooth® LE or WiFi to periodically transmit an identifying message. Road-based equipment detects and locates the smart phone. In a further aspect, the smart phone is alerted by roadside beacons and responds with identification information. Transaction processing may be performed either on the smart phone or by roadside or back office equipment. The system may be used for automated roadway tolling and monitoring and also for access control. A coded card may be scanned by the smart card to enter identification for access control.