crime Object Detection Dataset and Pre-Trained Model by bahria university

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Explore Dataset

Here are a few use cases for this project:

Law Enforcement Surveillance: This computer vision model could be used in CCTV monitoring and analysis to identify potential crime activities in real-time. It could detect people carrying weapons, or suspicious behavior that matches the criminal class.
Social Media Monitoring: The "Crime" model could scan and analyse social media content to detect activities suggesting potential threats or criminal activities, helping law enforcement quickly intervene.
Forensic Investigations: The model could assist forensic teams in examining photos or videos from crime scenes to identify people present, potential weapons used, and specific behaviors linked to the crime.
Security Systems: Computer vision models could be integrated into personal or commercial security systems to detect potential break-ins or criminal activities, triggering alarms or alerts.
Public Safety: In crowded public places like stadiums or events, this model could monitor the crowd for unusual behavior or potential threats, enhancing public safety and crowd management.

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{
                            crime-dp3x3_dataset,
                            title = { crime Dataset },
                            type = { Open Source Dataset },
                            author = { bahria university },
                            howpublished = { \url{ https://universe.roboflow.com/bahria-university-g0y7w/crime-dp3x3 } },
                            url = { https://universe.roboflow.com/bahria-university-g0y7w/crime-dp3x3 },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { may },
                            note = { visited on 2024-04-24 },
                            }