Traffic Scenario 2.0

Object Detection

Traffic Scenario 2.0 Computer Vision Project


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Explore Dataset

Here are a few use cases for this project:

  1. Traffic Management Systems: This model could be utilized in different global traffic management systems to classify the types of traffic on roads, enabling better traffic planning and congestion control.

  2. Autonomous Driving Technology: The "Traffic Scenario 2.0" model can be used in the development and enhancement of autonomous vehicle technologies. It can aid these systems in identifying different elements on the roads, facilitating safe navigation.

  3. Security and Surveillance: The model can be used in security cameras to monitor street activities, identify illegal actions such as street racing, and enforce traffic rules by identifying different classes of road users.

  4. Accident Analysis and Prevention: Based on the model's ability to identify different traffic classes, it can inform the design of advanced systems for predicting and preventing road accidents based on the interactions between different entities on the road.

  5. Urban Planning: City planners can use the insights from this model to better understand the flow and composition of traffic, which can inform the design of roads, bike lanes, and pedestrian areas.

Cite This Project

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

                            title = { Traffic Scenario 2.0 Dataset },
                            type = { Open Source Dataset },
                            author = { new-workspace-1wgnz },
                            howpublished = { \url{ } },
                            url = { },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2021 },
                            month = { dec },
                            note = { visited on 2024-02-29 },

Connect Your Model With Program Logic

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Last Updated

2 years ago

Project Type

Object Detection



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