GeminiGuard

An automated accident detection system that uses Gemini's multimodal capabilities to analyze crash data and alert emergency services with real-time context

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Inspiration

Road accidents are a major global issue, and often, the time lost between the crash and the arrival of emergency services can be the difference between life and death. As a first-year engineering student, I wanted to use technology to solve a real-world problem. I realized that while many cars have sensors, we don't always have an intelligent "eye" that can understand the context of a crash and alert the right people immediately. This inspired me to build GeminiGuard

What it does

GeminiGuard is an intelligent accident detection system powered by Gemini 3.0. Visual Detection: It monitors a video feed (like a dashboard camera or traffic cam) for signs of a collision. AI Analysis: When a potential incident is detected, it sends the video frames to the Gemini API. Gemini acts as the "brain," analyzing the severity of the crash and identifying if there is fire, overturned vehicles, or injured passengers. Emergency Alert: If a crash is confirmed, the system instantly sends an alert (via SMS/Email) with the location and details to emergency contacts

How we built it

We built the application using Python as our core language. Frontend: We used Streamlit to create a user-friendly dashboard where users can upload video feeds or view the live camera. Computer Vision: We used OpenCV to break the video into frames. The AI Brain: This is where the magic happens. We integrated Google AI Studio's Gemini API. We send the frames to the Gemini model with a specific prompt to analyze the situation. Alerts: We used [Mention tool, e.g., Twilio or simple Python libraries] to handle the messaging

Challenges we ran into

Since I am in my first year of B.Tech, working with advanced GenAI models was a steep learning curve. API Integration: Understanding how to send image data effectively to the Gemini API took some trial and error. Real-time Processing: Figuring out how to process video frames without making the system lag was difficult. Prompt Engineering: We had to tweak our prompts many times to ensure Gemini didn't give false alarms on minor traffic bumps

Accomplishments that we're proud of

Successfully connecting to the Gemini API and getting a response for the first time. Building a working prototype that can actually "see" an accident in a video video. Learning how to use Streamlit to build a web interface in just a few days

What we learned

Participating in this hackathon taught me that AI isn't just about chatbots—it's about "multimodal" understanding (seeing and reasoning). I learned the basics of API calls, Python application structure, and how to integrate different libraries like OpenCV and Google Generative AI.

What's next for GeminiGuard

IoT Integration: Connecting this software to a Raspberry Pi or Arduino to work on a real RC car or vehicle. GPS Module: Adding real-time GPS coordinates to the alert message. Voice Assistance: Using Gemini's audio capabilities to talk to the driver after a crash to check if they are conscious.

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