Crime alert

Inspiration

The Active Alert system was inspired by the need to prevent crimes before they occur. By leveraging advanced technologies like artificial intelligence and machine learning, we aimed to create a proactive solution that detects anomalies and alerts authorities in real-time, enabling swift action to prevent potential crimes.

What it does

Active Alert is a comprehensive crime anomaly detection system that uses AI-powered sensors and machine learning algorithms to detect and prevent potential crimes. The system performs the following functions:

•⁠ ⁠Weapon Detection: Identifies weapons such as guns, knives, and explosives using AI-powered sensors and computer vision algorithms. ⁠Real-time Alerts: Sends immediate alerts to authorities via a mobile app, enabling prompt response and prevention of potential crimes. ⁠Incident Reporting: Allows authorities to report incidents and provide additional context, enhancing the system's learning and improvement. •⁠ ⁠Integration with Existing Systems: Seamlessly integrates with existing security infrastructure, such as CCTV cameras and alarm systems, to enhance overall security.

How we built it

Active Alert was built using:

•⁠ ⁠Python for data processing •⁠ ⁠Deep learning framework YOLO •⁠ ⁠Deep learning model CNN (Convolutional neural network)

Challenges we ran into

During development, we faced several challenges, including:

•⁠ ⁠Ensuring high accuracy in weapon detection •⁠ ⁠Addressing false positives and false negatives •⁠ ⁠Integrating with existing law enforcement systems and infrastructure •⁠ ⁠Ensuring scalability and reliability

Accomplishments that we're proud of

we are proud that our application can detect various weapons with 92% accuracy

What's next for Untitled

Our future plans include:

•⁠ ⁠Expanding Active Alert to more cities and countries •⁠ ⁠Integrating with additional data sources, such as social media and surveillance cameras •⁠ ⁠Enhancing the system to detect other types of anomalies, such as suspicious behavior

Team members

Mansi

1. Research and Data Collection:
   • Gathered crime data and statistics
   • Identified types of crimes to focus on (e.g., theft, vandalism, assault)
   • Collected information on existing crime detection systems
2. System Design:
   • Defined system architecture and components
   • Designed database schema for crime data storage
   • Planned user interface (UI) and user experience (UX)
3. Alert System Development:
   • Developed alert notification system for authorities
   • Integrated with messaging services (e.g., SMS, email)
   • Implemented escalation protocols for urgent crimes

Gurliv

1. Crime Detection Algorithm Development:
   • Researched and implemented machine learning algorithms for crime detection
   • Trained models using collected data
   • Optimized algorithm performance
2. Sensor Integration and Data Processing:
   • Integrated sensors (e.g., CCTV, audio) for data collection
   • Developed data processing module for sensor data
   • Ensured data quality and consistency
3. System Testing and Deployment:
   • Conducted unit testing, integration testing, and system testing
   • Deployed system on cloud or local infrastructure
   • Ensured scalability and security

Shared Responsibilities:

1. Project Planning and Coordination:
   • Collaborated on project timeline, milestones, and tasks
   • Ensured smooth communication and progress updates
2. Documentation and Reporting:
   • Maintained project documentation (e.g., design documents, meeting notes)
   • Prepared reports on project progress and outcomes

Built With

• machine-learning
• python