EYECON - Context-based Detection of Mixed-Criticality Events using Computer Vision
Areas:
Fire:From lighter flames to accidental incidents, fires vary in severity from moderate to catastrophic, emphasizing the importance of readiness
Theft:Theft can range from minor incidents to major crimes, highlighting the need for vigilance and preventative measures
Crowd:From bustling crowds to packed events, crowd dynamics vary, emphasizing the importance of safety protocols and crowd management strategies
Traffic:Traffic accidents span from minor bumps to severe collisions, stressing the significance of safe driving habits.
Traffic
Violence
Fire Incidents
Theft
TARGET AUDIENCE:
- Banks
- Offices
- industries
- Petrol Pump
- Shopping Plaza
- Educational Institutes
How Does It Works?
Real-Time Data Capture:Our project seamlessly captures data in real-time.
Machine Learning Models: Cutting-edge ML models are employed to identify the context and criticality of events.
Context Identification:Events are categorized as moderate, critical, or catastrophic based on their context.
Versatile Application:Our system operates across various scenarios including fire incidents, traffic situations, crowd management, and theft detection..
Precision and Efficiency:By leveraging ML algorithms, our project ensures precise event identification and efficient response mechanisms.