Waterfront Object Detection Dataset and Pre-Trained Model by Waterfront dataset

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Explore Dataset

Here are a few use cases for this project:

Urban Planning Analysis: This model could be used for urban planning applications. By identifying the number of people in different areas along the riverfront, one can gather data on population density or popular locations. This can aid in the decision-making process of urban development, such as where to construct amenities or recreational areas.
Security Surveillance: Deploying this model in CCTV systems can prove beneficial for public safety and A.I.-powered surveillance. The model could monitor waterfront environments and potentially identify suspicious activities or overcrowding.
Tourism Management: By understanding which places people congregate in the most, tourism boards could use this information for tours or events. It can help in managing crowd control, enhancing the tourist experience, or promoting lesser-known city attractions.
Environmental Studies: This model could be used to correlate the number of people using a waterfront with environmental impact studies, such as littering or accelerated wear and tear on waterfront structures or ecosystems.
Public Health and Safety: In scenarios related to public health—for instance, during a pandemic—the model could be used to monitor and ensure that social distancing is being maintained in popular waterfront areas.

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{
                            waterfront_dataset,
                            title = { Waterfront Dataset },
                            type = { Open Source Dataset },
                            author = { Waterfront dataset },
                            howpublished = { \url{ https://universe.roboflow.com/waterfront-dataset/waterfront } },
                            url = { https://universe.roboflow.com/waterfront-dataset/waterfront },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { feb },
                            note = { visited on 2024-06-29 },
                            }