Computer Vision (CLIP/BLIP)Mobile Development (Flutter)Backend engineering
Publié il y a environ 12 heures
Stage
⏱️4-6 mois
💼Présentiel
📅Expire dans 13 jours
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Description du poste
Project overview
A complete smart-parking solution combining real-time computer vision and a user-friendly mobile application.
Street cameras analyze live video streams to detect vehicles and identify available parking slots using AI, with detected free spots displayed instantly in a Flutter mobile app.
Objectives: reduce parking search time, optimize city traffic flow and provide an efficient, modern parking experience.
Main responsibilities
Develop and train object detection/segmentation models to detect parked vehicles and infer free parking slots using frameworks such as TensorFlow or PyTorch.
Build and integrate a real-time computer vision pipeline (capture → detection → tracking → slot availability inference) using Python and OpenCV.
Design and implement REST APIs and backend services to serve detection results and persist data in PostgreSQL.
Integrate AI outputs with the Flutter mobile application so users can view live parking availability and receive updates before arriving.
Required profile & skills
Basic knowledge of Python and AI (object detection, segmentation, tracking) and understanding of computer vision pipelines.
Familiarity with Flutter mobile development and ability to integrate backend/AI outputs into the app UI.
Experience or willingness to work with OpenCV, TensorFlow or PyTorch for model development and inference.
Good problem-solving and analytical skills, plus creativity in building intelligent user experiences.
Technologies & tools
Languages / frameworks: Python, Flutter, any backend framework, PostgreSQL.
CV / AI: OpenCV, TensorFlow, PyTorch; model training, inference and tracking techniques.
Dev tools & cloud: REST APIs, GitHub, Slack, Visual Studio Code, cloud hosting (Azure).
Logistics
Internship type: Masters, Engineer (PFE).
Constraints: On Site, Individual (one student project).