labelling_taxibe Object Detection Dataset and Pre-Trained Model by opendata

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Explore Dataset

Here are a few use cases for this project:

Traffic Management: The "labelling_taxibe" model can be employed in traffic management systems to identify different types of vehicles and pedestrians in real-time. This data can then be used to analyze and improve traffic flow, infrastructure planning, and safety measures.
Accident Analysis: This model can be used in accident reconstruction scenarios. Authorities can input images or video footage from accident scenes to identify types of vehicles involved and pedestrian presence, helping to reconstruct the incident more accurately.
Automotive Industry: Car manufacturers could use this model for testing and developing the Advanced Driver Assistance Systems (ADAS) and autonomous driving systems.
Parking Management Systems: In parking facilities, the model can identify vehicle types to optimize parking space allocation, providing effective space management and potentially even dynamic pricing based on vehicle size.
Law Enforcement: The model can assist in enforcing traffic rules and regulations. For instance, it can identify whether a motorbike or bicycle is riding in a car lane, or if a truck is using a lane or area restricted to lighter vehicles or pedestrians.

Trained Model API

This project has a trained model available that you can try in your browser and use to get predictions via our Hosted Inference API and other deployment methods.

Cite This Project

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

@misc{
                            labelling_taxibe_dataset,
                            title = { labelling_taxibe Dataset },
                            type = { Open Source Dataset },
                            author = { opendata },
                            howpublished = { \url{ https://universe.roboflow.com/opendata/labelling_taxibe } },
                            url = { https://universe.roboflow.com/opendata/labelling_taxibe },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { jan },
                            note = { visited on 2024-04-18 },
                            }