Inspiration Monitoring environmental hazards like industrial gas leaks, chemical spills, and early-stage wildfires usually requires expensive, specialized hardware sensors that leave major blind spots. We realized that millions of standard CCTV cameras are already installed everywhere—they just lack the intelligence to spot danger. We built EcoPulse AI to give existing camera infrastructure an automated "brain" to catch environmental threats before they turn into catastrophes.
What it does EcoPulse AI transforms standard visual and infrared CCTV networks into real-time hazard detection nodes. Using computer vision models, the system actively monitors video feeds for anomalies like smoke, gas plumes, illegal toxic dumping, and thermal spikes. When a hazard is detected, it flags the exact camera location, assesses severity, and automatically dispatches real-time alerts to site managers and emergency responders.
How we built it Computer Vision: Trained YOLOv8 models using OpenCV to process and detect visual hazard markers in live video streams.
Backend: Built a high-throughput FastAPI backend in Python to handle concurrent camera feeds and process inferences with low latency.
Frontend Dashboard: Designed a responsive monitoring UI in React and Tailwind CSS that visualizes active camera locations and live hazard alerts.
Alert System: Integrated OpenAI’s API to automatically generate concise incident reports sent instantly via automated notification channels.
Challenges we ran into Latency & Processing Overhead: Processing multiple high-resolution video streams simultaneously put heavy strain on the pipeline. We had to optimize frame sampling rates and light-weight model parameters to maintain real-time speeds.
False Positives: Environmental variables like fog, harsh shadows, and vehicle exhaust triggered initial false alarms, requiring refined training data and confidence threshold filtering.
Accomplishments that we're proud of Successfully achieving sub-second hazard detection on live video feeds without requiring dedicated high-end hardware.
Building a full end-to-end prototype—from raw video input to automated incident report generation—within the short hackathon timeframe.
What we learned Techniques for optimizing computer vision inference pipelines to handle live streaming data efficiently.
How to balance model confidence thresholds to minimize false alarms in outdoor, unpredictable environments.
What's next for Ecopulse AI Integrating direct thermal and multispectral camera integration for precision night-vision and invisible gas leak detection.
Partnering with local municipalities and industrial parks to run real-world pilot tests on existing infrastructure.
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