Master's Thesis: Multi-Object Tracking of Bulk Materials at High Occupancy Densities

Fraunhofer-Gesellschaft · Karlsruhe

ThesisGerman needed: not said

Still open on 3 Nov

You apply on the company's own website.

Key facts

Where you work: Company and home
Subject you study: Computer Science, Mathematics, Physics, Electrical Engineering, Information Technology

You plan and carry out sensor data recordings on industrial-like sorting systems with a high-resolution area-scan camera under varying occupancy densities
You implement instance segmentation methods and combine them with modern multi-object tracking approaches
You systematically investigate the limits of tracking accuracy depending on occupancy density and object overlap.
You test and validate your developed approaches directly on real sensor data from our industrial bulk material demonstrators
You contribute your own ideas – we define the exact focus of the work together based on your interests

You are enrolled (university / university of applied sciences) and study computer science, mathematics, physics, electrical engineering and information technology or a comparable field
You have good Python programming skills and ideally first experience with common deep learning frameworks (e.g. PyTorch)
You have great interest in topics such as multi-object tracking, instance segmentation, machine learning and industrial image analysis
You enjoy programming and like to learn new things
You bring scientific curiosity and enjoy professional discussions and exchange in the team

Central location: only a few minutes' walk from KIT Campus Süd and very good public transport connections.
Flexible work: combination of mobile work and work on site in agreement with your supervisor
Individual support: benefit from our professional guidance and open, communicative exchange in the team.
Exciting tasks and room for ideas: a high degree of personal responsibility and the chance to actively contribute your own ideas.

You plan and carry out sensor data recordings on industrial-like sorting systems with a high-resolution area-scan camera under varying occupancy densities
You implement instance segmentation methods and combine them with modern multi-object tracking approaches
You systematically investigate the limits of tracking accuracy depending on occupancy density and object overlap.
You test and validate your developed approaches directly on real sensor data from our industrial bulk material demonstrators
You contribute your own ideas – we define the exact focus of the work together based on your interests

You are enrolled (university / university of applied sciences) and study computer science, mathematics, physics, electrical engineering and information technology or a comparable field
You have good Python programming skills and ideally first experience with common deep learning frameworks (e.g. PyTorch)
You have great interest in topics such as multi-object tracking, instance segmentation, machine learning and industrial image analysis
You enjoy programming and like to learn new things
You bring scientific curiosity and enjoy professional discussions and exchange in the team

Central location: only a few minutes' walk from KIT Campus Süd and very good public transport connections.
Flexible work: combination of mobile work and work on site in agreement with your supervisor
Individual support: benefit from our professional guidance and open, communicative exchange in the team.
Exciting tasks and room for ideas: a high degree of personal responsibility and the chance to actively contribute your own ideas.