People ID Object Detection Dataset and Pre-Trained Model by persons

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Explore Dataset

Here are a few use cases for this project:

Retail Shop Analysis: The "People ID" computer vision model can be used to analyze customer behavior in a retail store. For example, it can track the movement of customers, identify their browsing and purchasing patterns, and help analyze peak hours and areas of the store that receive heavy footfall.
Crowd Management: In large events like concerts, festivals, or sports gatherings, the model can be used to identify and count the number of attendees, also monitor crowd movement patterns for safety and security purposes.
Security Surveillance: Use the model to enhance CCTV surveillance in public spaces or high-security areas. The technology can help in identifying unauthorized persons or unusual behaviors, contributing to safety and security.
Health Compliance Monitoring: The model can be used in hospitals, offices, or public spaces to ensure compliance with health regulations like mask compliance during a pandemic, or tracking people's movements to trace potential infection transmission.
Smart Home Applications: In smart home applications, the model can be used to identify specific family members for personalized experiences or detect unfamiliar faces for security reasons.

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{
                            people-id_dataset,
                            title = { People ID Dataset },
                            type = { Open Source Dataset },
                            author = { persons },
                            howpublished = { \url{ https://universe.roboflow.com/persons/people-id } },
                            url = { https://universe.roboflow.com/persons/people-id },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { jul },
                            note = { visited on 2024-05-02 },
                            }