Passenger Counter Object Detection Dataset and Pre-Trained Model by Passenger Counter Project

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Explore Dataset

Here are a few use cases for this project:

Public Transportation Management: The Passenger Counter model can be utilized by public transportation systems like buses, trains, and subways to monitor and track passenger numbers in real-time. This can help transport agencies optimize routes, schedules, and operating capacities to better serve the public and improve efficiency.
Airport Passenger Analytics: Airports can use the Passenger Counter model to manage and understand passenger flows at various locations such as check-in counters, boarding gates, and baggage claim areas. This can help streamline processes, enhance security, and provide insights for infrastructure planning.
Retail Customer Tracking: Retail stores and shopping centers can implement the Passenger Counter model to track and assess foot traffic patterns, customer density, and visit duration. This can provide valuable insights for store layout optimization, product placement, and targeted marketing campaigns.
Event and Venue Management: Organizers of events, conferences, and concerts can employ the Passenger Counter model for accurate crowd monitoring and control. Real-time passenger counts can enable more efficient management of entrances, exits, and people-flow during the events, increasing safety and improving the overall attendee experience.
Smart City Initiatives: City planners and urban management agencies can integrate the Passenger Counter model as part of smart city initiatives to analyze pedestrian traffic patterns, monitor crowded areas, and inform decisions on public space utilization, infrastructure development, and overall city planning.

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{
                            passenger-counter_dataset,
                            title = { Passenger Counter Dataset },
                            type = { Open Source Dataset },
                            author = { Passenger Counter Project },
                            howpublished = { \url{ https://universe.roboflow.com/passenger-counter-project/passenger-counter } },
                            url = { https://universe.roboflow.com/passenger-counter-project/passenger-counter },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2022 },
                            month = { aug },
                            note = { visited on 2024-06-26 },
                            }