Computer vision is one of the major tasks and applications of artificial intelligence (AI). Gaining hands-on experience is therefore of great importance for future AI developers. In the Tracking Olympiad, students use the latest object detection and tracking algorithms to track a freely, randomly moving object ("HexBug") in a given arena. The students will be provided with a set of videos that contain the ground-truth positional information and implement an own tracking technique. At the beginning of the seminar, all students are divided into teams that compete with each other to find the best strategy for tracking the HexBug. The team's tracking prediction needs to be an algorithm that incorporates each student's tracking algorithm. The team's score will be evaluated by applying the team's tracking algorithm to previously unseen/withheld videos. Further, the team acquires and annotates own data to improve their tracking algorithms. Each team selects videos that are tested by the other teams' algorithm and are subsequently ranked similar to a soccer league table. The aim of this seminar is to enable each student to have an own AI-powered tracking algorithm that is an integral part of a team solution. The Tracking Olympiad consists of two sessions in a given week, one with a journal club explaining AI tracking concepts by students and one for open Q&A depending on the individual student's progress with voluntary developmental time.
AIBE Seminar Room, Werner-von-Siemens-Str. 61, 91054 Erlangen
will be able to create own code
are able to create acquire and annotate own data
can document their code
will strengthen their team skills
can develop tracking algorithms
will learn about the latest AI methods
can present complex topics
can extract relevant information from journal papers
Examination, seminar performance, duration (in minutes): 20, graded, 5 ECTS Share of the calculation of the module grade: 100.0 %
Talk (presenting paper/video) 20 min, written report 10-15 pages, valued 50% talk and 50% written report for grading
Examination language: English
This publication (in German) collects the experiences of the KI-Campus peers incorporating digital, open-licensed, and freely available learning resources on AI in university teaching.