[go: up one dir, main page]

Abbas et al., 2024 - Google Patents

Vision based intelligent traffic light management system using Faster R‐CNN

Abbas et al., 2024

View PDF @Full View
Document ID
17238426316184584161
Author
Abbas S
Khan M
Zhu J
Sarwar R
Aljohani N
Hameed I
Hassan M
Publication year
Publication venue
CAAI Transactions on Intelligence Technology

External Links

Snippet

Transportation systems primarily depend on vehicular flow on roads. Developed countries have shifted towards automated signal control, which manages and updates signal synchronisation automatically. In contrast, traffic in underdeveloped countries is mainly …
Continue reading at ietresearch.onlinelibrary.wiley.com (PDF) (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
    • G06K9/36Image 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
    • 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
    • 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
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • GPHYSICS
    • G08SIGNALLING
    • G08GTRAFFIC CONTROL SYSTEMS
    • G08G1/00Traffic control systems for road vehicles
    • G08G1/01Detecting movement of traffic to be counted or controlled
    • 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
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management

Similar Documents

Publication Publication Date Title
Lin et al. A Real‐Time Vehicle Counting, Speed Estimation, and Classification System Based on Virtual Detection Zone and YOLO
Ye et al. Lane detection method based on lane structural analysis and CNNs
Razi et al. Deep learning serves traffic safety analysis: A forward‐looking review
Gupta et al. A novel finetuned YOLOv6 transfer learning model for real-time object detection
Kim et al. Extracting vehicle trajectories using unmanned aerial vehicles in congested traffic conditions
Ghasemi Darehnaei et al. SI‐EDTL: swarm intelligence ensemble deep transfer learning for multiple vehicle detection in UAV images
Parmar et al. Deeprange: deep‐learning‐based object detection and ranging in autonomous driving
Ke et al. Advanced framework for microscopic and lane‐level macroscopic traffic parameters estimation from UAV video
Youssef et al. Automatic vehicle counting and tracking in aerial video feeds using cascade region-based convolutional neural networks and feature pyramid networks
Azimjonov et al. A vision-based real-time traffic flow monitoring system for road intersections
Wang Vehicle image detection method using deep learning in UAV video
Abbas et al. Vision based intelligent traffic light management system using Faster R‐CNN
Ka et al. Study on the framework of intersection pedestrian collision warning system considering pedestrian characteristics
Athanesious et al. Detecting abnormal events in traffic video surveillance using superorientation optical flow feature
Guo et al. Dense traffic detection at highway-railroad grade crossings
Liu et al. Cooperative and comprehensive multi-task surveillance sensing and interaction system empowered by edge artificial intelligence
Andika et al. Improved feature extraction network in lightweight YOLOv7 model for real-time vehicle detection on low-cost hardware
Yu et al. ECCNet: Efficient chained centre network for real‐time multi‐category vehicle tracking and vehicle speed estimation
Liu et al. MDFD2-DETR: A Real-Time Complex Road Object Detection Model Based on Multi-Domain Feature Decomposition and De-Redundancy
Prethi et al. Edge based intelligent secured vehicle filtering and tracking system using YOLO and EasyOCR
Deshmukh et al. Vehicle detection in diverse traffic using an ensemble convolutional neural backbone via feature concatenation
Zarei et al. Real‐time vehicle detection using segmentation‐based detection network and trajectory prediction
Gao et al. Whether and how congested is a road? Indices, updating strategy and a vision‐based detection framework
Zheng et al. A complex roadside object detection model based on multi-scale feature pyramid network
Sasikala et al. Toward a deep CNN and RS‐GOA framework for vehicle detection, traffic flow estimation, and optimal path selection from surveillance videos