Siren Eyes: AI-Powered Emergency Vehicle Detection

Problem Statement

1. Severe Ambulance Delays: Traffic congestion causes critical delays in emergency response.

2. Inaccurate Existing Systems: Single-mode detection (only siren or video) leads to false positives.

3. Manual Traffic Management: Current systems are slow, manual, and unresponsive.

Objective

• Ensure faster ambulance movement by accurate detection and dynamic traffic control.

Solution: Hybrid Detection System

• Dual-Mode Detection:

  1. Audio Recognition: Keras-based model detects siren sounds.

  2. Video Processing: YOLOv8 detects ambulances in video frames.

• Confirmation Logic:

  Only if both audio and video detections match (≥70% confidence), the system acts.

• Automated Traffic Control:

  Smartly changes traffic signals to prioritize ambulances in real-time.

Working Flow and Tech stack

1. Upload real-time traffic video through GUI.

2. Extract and analyze audio (KERAS model).

3. Detect ambulances in frames (YOLOv8, OpenCV).

4. Cross-validate both detections for maximum accuracy.

Impact

• Reduce average ambulance response time from 17 minutes to 8 minutes.

• Increase survival rate by up to 63%.

Future Scope

1. Predict ambulance arrival times at signals.

2. Instead of uploading a video, this model can work with integrated real-time cameras.

3. Enable coordination between multiple traffic signals via cloud computing.

4. Expand detection to other emergency vehicles like fire trucks and police vans.

Conclusion

Siren Eyes is a step toward smarter cities — saving lives by using AI to optimize emergency traffic management.