[go: up one dir, main page]

Tu, 2023 - Google Patents

Research on Road Traffic State Discrimination Technology based on RFID Electronic License Plate

Tu, 2023

Document ID
14714307399517903325
Author
Tu J
Publication year
Publication venue
2023 3rd International Conference on Intelligent Technologies (CONIT)

External Links

Snippet

The research focus of RFID technology based on license plate is to improve service quality, reduce traffic accidents and improve road safety. It has been proved that RFID based technology can be used to improve traffic control and reduce congestion in urban areas. The …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6267Classification techniques
    • G06K9/6268Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00624Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
    • G06K9/00771Recognising scenes under surveillance, e.g. with Markovian modelling of scene activity
    • GPHYSICS
    • G08SIGNALLING
    • G08GTRAFFIC CONTROL SYSTEMS
    • G08G1/00Traffic control systems for road vehicles
    • G08G1/01Detecting movement of traffic to be counted or controlled
    • G08G1/0104Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125Traffic data processing
    • G08G1/0129Traffic data processing for creating historical data or processing based on historical data
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/68Methods or arrangements for recognition using electronic means using sequential comparisons of the image signals with a plurality of references in which the sequence of the image signals or the references is relevant, e.g. addressable memory
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46Extraction of features or characteristics of the image
    • GPHYSICS
    • G08SIGNALLING
    • G08GTRAFFIC CONTROL SYSTEMS
    • G08G1/00Traffic control systems for road vehicles
    • G08G1/01Detecting movement of traffic to be counted or controlled
    • G08G1/04Detecting movement of traffic to be counted or controlled using optical or ultrasonic detectors
    • GPHYSICS
    • G08SIGNALLING
    • G08GTRAFFIC CONTROL SYSTEMS
    • G08G1/00Traffic control systems for road vehicles
    • G08G1/01Detecting movement of traffic to be counted or controlled
    • G08G1/017Detecting movement of traffic to be counted or controlled identifying vehicles
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/20Image acquisition
    • G06K9/32Aligning or centering of the image pick-up or image-field
    • G06K9/3233Determination of region of interest
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models

Similar Documents

Publication Publication Date Title
Li et al. Real-time crash risk prediction on arterials based on LSTM-CNN
Dou et al. A hybrid CEEMD-GMM scheme for enhancing the detection of traffic flow on highways
Shine et al. Automated detection of helmet on motorcyclists from traffic surveillance videos: a comparative analysis using hand-crafted features and CNN
Simoncini et al. Vehicle classification from low-frequency GPS data with recurrent neural networks
Yang et al. Image-based visibility estimation algorithm for intelligent transportation systems
Jiang et al. Investigating macro-level hotzone identification and variable importance using big data: A random forest models approach
Sliwa et al. The channel as a traffic sensor: Vehicle detection and classification based on radio fingerprinting
Kim et al. Interpretable machine-learning models for estimating trip purpose in smart card data
Mehrannia et al. Deep representation of imbalanced spatio‐temporal traffic flow data for traffic accident detection
Wang et al. Research on key technologies of intelligent transportation based on image recognition and anti-fatigue driving
Sathya et al. A framework for designing unsupervised pothole detection by integrating feature extraction using deep recurrent neural network
Liu et al. Learning traffic as images for incident detection using convolutional neural networks
Barman et al. Alternative method for identifying crash hotspot using detailed crash information from First Information Report (FIR)
Zhao et al. Context-guided coarse-to-fine detection model for bird nest detection on high-speed railway catenary
Balavinodhan et al. Detection of Traffic Violation and Vechicle Number Plate Using Computer Vision
He et al. Applications of deep learning techniques for pedestrian detection in smart environments: a comprehensive study
Tu Research on Road Traffic State Discrimination Technology based on RFID Electronic License Plate
CN120726802A (en) A traffic flow state recognition method based on multi-source heterogeneous data sources
CN119007131B (en) Roadside digital video monitoring method and system based on deep learning
Huang SVM‐Based Real‐Time Identification Model of Dangerous Traffic Stream State
Lucas et al. Online travel time estimation without vehicle identification
Sliwa et al. Leveraging the channel as a sensor: Real-time vehicle classification using multidimensional radio-fingerprinting
Srinivas et al. Integrating smart technologies for enhanced traffic management and road safety: a data-driven approach
Thakkar et al. Automated Pothole Detection using Transfer Learning
Gao et al. Whether and how congested is a road? Indices, updating strategy and a vision‐based detection framework