Intelligent Video Surveillance System Enhancement
Budget: ₹600 – ₹1,500 INR
Project Title: Intelligent Video Surveillance System (IVSS)
Project Overview
We want to build an AI-powered real-time multi-camera surveillance system from scratch using Python and Flask. The system should detect motion, objects (like people or vehicles), and faces, and generate alerts based on user-configured settings.
Key Requirements
• Support for multiple camera feeds (local webcam + IP cameras like DroidCam).
• Real-time motion detection, object detection (YOLOv8), and face recognition.
• Ability to configure which detection modules (motion/object/face) are enabled per camera.
• Web-based dashboard with the following pages:
o Live Monitoring (camera feed)
o Alerts section (log of all alerts)
o Settings (add/remove cameras, toggle detections)
o Face Registration (upload face for recognition)
• Alerts should be stored in a database and optionally send:
o Desktop notifications
o Email alerts (with image attachment)
• Basic analytics view for alert summary (optional).
Tech Stack (Preferred)
• Backend: Python + Flask + Multiprocessing
• Frontend: HTML/CSS (Jinja2 templates), JS
• AI: OpenCV, Ultralytics YOLOv8, face_recognition (dlib)
• Database: SQLite
• Notification: Plyer, SMTP (Gmail)
What You Need to Build
• Camera feed handling using multiprocessing and shared memory
• Detection modules (modular design per detection type)
• Real-time alert system with logging
• Flask web UI with all mentioned features
• Cross-platform support (Linux + Windows)
• Optional: Docker setup for easy deployment
(Half of project is completed . Need to work' on remaining project)
Project Overview
We want to build an AI-powered real-time multi-camera surveillance system from scratch using Python and Flask. The system should detect motion, objects (like people or vehicles), and faces, and generate alerts based on user-configured settings.
Key Requirements
• Support for multiple camera feeds (local webcam + IP cameras like DroidCam).
• Real-time motion detection, object detection (YOLOv8), and face recognition.
• Ability to configure which detection modules (motion/object/face) are enabled per camera.
• Web-based dashboard with the following pages:
o Live Monitoring (camera feed)
o Alerts section (log of all alerts)
o Settings (add/remove cameras, toggle detections)
o Face Registration (upload face for recognition)
• Alerts should be stored in a database and optionally send:
o Desktop notifications
o Email alerts (with image attachment)
• Basic analytics view for alert summary (optional).
Tech Stack (Preferred)
• Backend: Python + Flask + Multiprocessing
• Frontend: HTML/CSS (Jinja2 templates), JS
• AI: OpenCV, Ultralytics YOLOv8, face_recognition (dlib)
• Database: SQLite
• Notification: Plyer, SMTP (Gmail)
What You Need to Build
• Camera feed handling using multiprocessing and shared memory
• Detection modules (modular design per detection type)
• Real-time alert system with logging
• Flask web UI with all mentioned features
• Cross-platform support (Linux + Windows)
• Optional: Docker setup for easy deployment
(Half of project is completed . Need to work' on remaining project)