Project Overview

• Develop an intelligent parking management system that performs real-time vehicle detection, identifies available parking spaces, and tracks entry/exit movements.
• System includes a React-based web interface for live visualization and MongoDB for structured data storage; target is an accurate, efficient solution testable in real-world scenarios.

Technical Objectives & Tasks

• Implement modern object detection algorithms (YOLO) with Python and OpenCV to detect vehicles and free parking slots in video streams.
• Design and integrate tracking for entry/exit movements, ensure real-time processing requirements, and connect detection outputs to a MongoDB backend for storage of events and metadata.

Required Skills & Technologies

• Strong Python skills, experience in computer vision and deep learning, familiarity with YOLO architectures and object detection pipelines.
• Experience with PyTorch or TensorFlow, OpenCV for image/video processing, React for front-end live visualization, and MongoDB for data persistence.

Deliverables & Evaluation

• Working prototype capable of processing live or recorded video with accurate vehicle detection, parking space availability status, and entry/exit logs.
• A React dashboard showing live results, a MongoDB schema for stored events, code repository, documentation, and demonstration in a real-world or simulated environment.

How to Apply

• Send your application to jobs@visshopai.com with the subject "Candidature — projet 1 : Real-Time Computer Vision System for Smart Parking Management PFE".
• You can also apply online via: https://lnkd.in/duKj9p6S

Additional Details

• Duration: 6 months (full PFE), Level: Bac +5, Number of interns: 1.
• Preferred candidates have prior projects or experience with YOLO-based detection, real-time inference optimization, and full-stack integration (backend DB + React frontend).