ALPR Computer Vision Project

ALPR

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Description

Here are a few use cases for this project:

  1. Traffic Surveillance Systems: The ALPR model could serve as a critical tool for ITS (Intelligent Transportation Systems) in streaming live feeds from various traffic cameras and detecting, analyzing, and recording license plates.

  2. Parking Management: It can be utilized for auto-identification of license plates at parking garages and lots for smoother vehicle entry and exit recording. This would contribute to improved management and potential revenue assurance.

  3. Law Enforcement: ALPR could aid police and security forces in locating stolen vehicles, identifying vehicles involved in criminal activities, or managing suspect car watchdog lists.

  4. Border Control: At border checkpoints, this model could be used for automatic plate recognition, consequently reducing human error and speeding up border control operations.

  5. Road Toll Collection: ALPR could be integrated into the toll collection system to automatically identify license plates, leading to automated and more efficient toll payments without stopping the vehicle.

Supervision

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Cite This Project

LICENSE
CC BY 4.0

If you use this dataset in a research paper, please cite it using the following BibTeX:

                        @misc{
                            alpr-zjhsm_dataset,
                            title = { ALPR Dataset },
                            type = { Open Source Dataset },
                            author = { ALPR },
                            howpublished = { \url{ https://universe.roboflow.com/alpr-tmla1/alpr-zjhsm } },
                            url = { https://universe.roboflow.com/alpr-tmla1/alpr-zjhsm },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2022 },
                            month = { jan },
                            note = { visited on 2024-11-13 },
                            }
                        
                    

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