Title: Real-Time Web-Based Visualization of Vehicle and Pedestrian Detections with Trajectories on a Map Overlay

Introduction: The increasing deployment of deep learning-based detection systems in urban environments necessitates efficient and scalable visualization solutions for vehicle and pedestrian detections. This thesis aims to develop a web-based application that visualizes real-time traffic participant detections and trajectories on a street or satellite map. The system will overlay detected positions, classifications (e.g., car, truck, bus, pedestrian, bicyclist), and trajectories on a bird’s-eye or isometric view map. A key focus will be on identifying suitable map providers and visualization technologies, ensuring high performance and scalability. A prototype implementation will be developed and evaluated based on visualization efficiency, usability, and real-time performance.

Objectives:

• Literature Review: Research existing techniques for visualizing traffic data in real-time applications.
• Technology Evaluation: Identify and compare suitable map providers (e.g., OpenStreetMap, Google Maps, Mapbox) and visualization technologies (e.g., WebGL, Three.js, Deck.gl), along with GIS frameworks.
• System Design: Develop a web-based architecture capable of handling large numbers of traffic participants in real time.
• Prototype Implementation: Build a proof-of-concept application that ingests geolocation data and displays real-time detections and trajectories.
• Performance Evaluation: Assess the system’s performance in terms of rendering speed, accuracy, and scalability.

Your Profile:

• Required Background Studies in Computer Science, Software Engineering, Information Technology, Geoinformatics or related fields
• Fluent English skills
• Interest in technology
• Willingness and ability to work independently
• Excellent communication and teamwork skills
• Conscientiousness and reliability
• Strong analytical skills with a precise and structured approach
• Start: Immediately
• Duration: 3–6 months
• Successful completion of the master’s thesis will be rewarded with €3,000.

Contact:

Edwin.Fruehwirth@kapsch.net