Case studyCompleted
Computer Vision Surveillance System
Built an end-to-end computer vision research system for real-time object detection, threat classification and alert generation.
PythonPyTorchYOLOv5/v8OpenCVFlaskNumPyLinux
Project summary
Final-year research project using YOLO, OpenCV and Flask for real-time object detection, threat classification and alerting.
Domain
computer vision
Project Type
final-year research
Problem
- Urban surveillance workflows often rely on manual monitoring and delayed threat recognition.
Architecture
- Problem-first framing so reviewers understand the operating context before implementation.
- Implementation details are tied to the stack and workflows in the project record.
- Sensitive client and company details are sanitized while preserving technical credibility.
- Business value and measurable outcomes are highlighted where evidence exists.
Key Decisions
- Use only completed work supported by the résumé or public repository evidence.
- Keep project claims specific, factual, and proportionate to available proof.
- Separate confidential client or company information from public technical summaries.
- Prioritize implementation decisions and outcomes over decorative presentation.
Implementation
- Built a YOLO-based real-time object detection system with Flask API, OpenCV stream processing and automated alerting.
- Documented the stack, business context, and relevant operational outcomes.
- Prepared a concise recruiter summary and safe public case-study presentation.
Quality Gates
- Public records include the problem, solution, stack, business value, and evidence status.
- Sensitive details remain intentionally summarized.
- No unsupported performance, certification, or capability claim is included.
Results
- Demonstrated functional automated threat recognition with a real-time inference and alert pipeline.
- Demonstrates completed work rather than tutorial or study-plan output.
- Supports software, backend, payments, business-systems, infrastructure, or computer-vision roles.
Future Improvements
- Add screenshots or architecture diagrams where safe to publish.
- Add demo videos for public-facing work.
- Attach additional verified performance or business-impact metrics when available.
Public Evidence
- Monitoring
- Docs