UAV Facial Detection Simulation Development
Budget: £20 – £250 GBP
I need a developer to create a simulation-based UAV/drone facial detection and tracking system. I am not building or flying a real drone. The aim is to create a software simulation where a virtual drone camera moves through an environment and detects/tracks human faces from the camera feed.
The project should be easy to understand, realistic to complete quickly, and use free or low-cost software.
The preferred approach is:
Option 1 — Best/easiest preferred route:
Use Webots for the drone/3D simulation, with a virtual camera attached to a simulated UAV. Then use Python + OpenCV + MediaPipe or another lightweight face detector to detect and track faces from the simulated camera feed.
Option 2 — Acceptable simpler route:
If Webots is too difficult, create a simplified Python/OpenCV simulation using UAV-style video footage or a custom 3D/animated environment that simulates a drone camera moving above/around people. It still needs to clearly look like a drone-camera facial detection/tracking demo.
The system should include:
• A simulated UAV/drone camera view
• Face detection from the simulated camera feed
• Face tracking across frames
• Bounding boxes around detected faces
• Basic tracking IDs if possible
• Output video showing the detection/tracking results
• Performance results such as FPS, number of faces detected, detection confidence if available, and tracking stability
• Screenshots of the simulation and detection output
• Clear setup instructions so I can run it myself
The final delivery must include:
1. Working source code
o Python scripts
o Any Webots world/project files if used
o Requirements file, e.g. requirements.txt
o Clear folder structure
2. Step-by-step documentation
o Exactly what software was installed
o Which websites were used
o How the simulation was created
o How the virtual drone/camera was set up
o How the face detection/tracking code works
o How to run everything from start to finish
o Include screenshots at each major stage
3. Final outputs
o Annotated output video showing faces being detected/tracked
o Screenshots of the running simulation
o Screenshots of the Python/OpenCV detection output
o CSV or simple results file showing FPS/detections per frame if possible
o A short explanation of limitations and possible improvements
Suggested software/websites:
• Webots: for the drone/robot simulation
• Python: main programming language
• OpenCV: video processing and drawing bounding boxes
• MediaPipe Face Detection or OpenCV YuNet: face detection
• NumPy / Pandas / Matplotlib: for results and graphs
• VS Code: code editor
• GitHub or ZIP folder: for final code delivery
Important requirements:
• Please keep the project simple and practical.
• Do not make it depend on expensive hardware.
• Do not use paid APIs.
• Do not require a real drone.
• Use clear comments in the code.
• The simulation and code must be easy for a beginner/intermediate student to run.
• The project should be completed in a way that can be explained clearly with screenshots and a written technical breakdown.
I need someone who can provide both the working simulation/code and a detailed explanation of exactly what they did, including screenshots and setup steps.
The project should be easy to understand, realistic to complete quickly, and use free or low-cost software.
The preferred approach is:
Option 1 — Best/easiest preferred route:
Use Webots for the drone/3D simulation, with a virtual camera attached to a simulated UAV. Then use Python + OpenCV + MediaPipe or another lightweight face detector to detect and track faces from the simulated camera feed.
Option 2 — Acceptable simpler route:
If Webots is too difficult, create a simplified Python/OpenCV simulation using UAV-style video footage or a custom 3D/animated environment that simulates a drone camera moving above/around people. It still needs to clearly look like a drone-camera facial detection/tracking demo.
The system should include:
• A simulated UAV/drone camera view
• Face detection from the simulated camera feed
• Face tracking across frames
• Bounding boxes around detected faces
• Basic tracking IDs if possible
• Output video showing the detection/tracking results
• Performance results such as FPS, number of faces detected, detection confidence if available, and tracking stability
• Screenshots of the simulation and detection output
• Clear setup instructions so I can run it myself
The final delivery must include:
1. Working source code
o Python scripts
o Any Webots world/project files if used
o Requirements file, e.g. requirements.txt
o Clear folder structure
2. Step-by-step documentation
o Exactly what software was installed
o Which websites were used
o How the simulation was created
o How the virtual drone/camera was set up
o How the face detection/tracking code works
o How to run everything from start to finish
o Include screenshots at each major stage
3. Final outputs
o Annotated output video showing faces being detected/tracked
o Screenshots of the running simulation
o Screenshots of the Python/OpenCV detection output
o CSV or simple results file showing FPS/detections per frame if possible
o A short explanation of limitations and possible improvements
Suggested software/websites:
• Webots: for the drone/robot simulation
• Python: main programming language
• OpenCV: video processing and drawing bounding boxes
• MediaPipe Face Detection or OpenCV YuNet: face detection
• NumPy / Pandas / Matplotlib: for results and graphs
• VS Code: code editor
• GitHub or ZIP folder: for final code delivery
Important requirements:
• Please keep the project simple and practical.
• Do not make it depend on expensive hardware.
• Do not use paid APIs.
• Do not require a real drone.
• Use clear comments in the code.
• The simulation and code must be easy for a beginner/intermediate student to run.
• The project should be completed in a way that can be explained clearly with screenshots and a written technical breakdown.
I need someone who can provide both the working simulation/code and a detailed explanation of exactly what they did, including screenshots and setup steps.