Case studyCompleted
AI-Powered Surveillance System
Built an end-to-end AI surveillance system with YOLO, OpenCV, Flask inference API and automated alert generation.
PythonPyTorchYOLOv5/v8OpenCVFlaskNumPyLinux
Project summary
Final-year research project implementing real-time object detection and threat recognition using YOLO, OpenCV and Flask.
Domain
Computer Vision
Project Type
Final-year research
Problem
- Urban surveillance workflows often rely on manual monitoring and delayed threat recognition.
Architecture
- Problem-first presentation: the case study opens with the operational or engineering pain point, not a UI effect.
- Implementation details are summarized through stack, workflow, integrations, data handling and deployment context.
- Sensitive client/company details are sanitized while preserving real business value and technical credibility.
- Every project record links stack, proof, engineering evidence and measurable impact where available.
Key Decisions
- Use real resume evidence only: client work, company work, academic research and completed portfolio platform work.
- Keep private learning plans out of the public portfolio.
- Avoid overclaiming: status, proof and metrics must reflect actual evidence.
- Prioritize readable business value and implementation decisions over decorative presentation.
Implementation
- Built a YOLO-based real-time object detection system with Flask API, OpenCV stream processing and automated alerting.
- Stack used: Python, PyTorch, YOLOv5/v8, OpenCV, Flask, NumPy, Linux.
- Documented the project with a recruiter summary, business value and engineering evidence.
- Prepared the project for public case-study presentation with safe disclosure boundaries.
Quality Gates
- Testing evidence is limited or not public for this project.
- CI/CD is not public or not applicable for this project.
- Deployment proof is not public or not applicable.
- Public wording avoids private planning language and keeps the portfolio professional.
Results
- Demonstrated functional automated threat recognition with real-time inference and alert pipeline.
- Strengthens the portfolio through real-world experience instead of tutorial-style claims.
- Supports positioning across software engineering, IT systems, business automation and applied AI/ML engineering.
Future Improvements
- Add screenshots or diagrams where safe to publish.
- Add demo videos for public-facing work.
- Attach more measurable performance, reliability or business impact metrics when available.
- Keep client/company-sensitive details sanitized.
Public Evidence
- Monitoring
- Docs