Tracking Olympiad (TRACO)5 ECTS
(englische Bezeichnung: Tracking Olympiad)
(Prüfungsordnungsmodul: Tracking Olympiad)

Lehrveranstaltungen:

• • Tracking Olympiad
    (Seminar, 4 SWS, Andreas Kist et al., Di, 10:15 - 11:45, Raum n.V.; Fr, 9:15 - 10:45, Raum n.V.; AIBE Seminar Room, Werner-von-Siemens-Str. 61, 91054 Erlangen)

Empfohlene Voraussetzungen:

Es wird empfohlen, folgende Module zu absolvieren, bevor dieses Modul belegt wird:

Data Science Survival Skills (WS 2021/2022)
Deep Learning (WS 2021/2022)
Pattern Recognition (WS 2021/2022)
Introduction to Machine Learning (WS 2021/2022)
Machine Learning for Engineers I: Introduction to Methods and Tools (WS 2021/2022)
Machine Learning for Engineers II: Advanced Methods (WS 2021/2022)

Inhalt:

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 utilize 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 which 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 developing 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.

Lernziele und Kompetenzen:

Students

• 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 latest AI methods

• can present complex topics

• can extract relevant information from journal papers

Literatur:

• Burger and Burge, Principles of Digital Image Processing (all volumes)

• Howes and Minichino, Learning OpenCV 4 Computer Vision with Python 3

• Sebastian Raschka, Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2

• Aurélien Géron, Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow

• Pereira et al., Quantifying behaviour to understand the brain, Nat Neurosci 2020

Verwendbarkeit des Moduls / Einpassung in den Musterstudienplan:

1. Data Science (Master of Science)
   (Po-Vers. 2021w | Gesamtkonto | Anwendungsfächer | Artificial intelligence in biomedical engineering (AIBE) | Tracking Olympiad)

Studien-/Prüfungsleistungen:

Tracking Olympiad (Prüfungsnummer: 76121)

(englischer Titel: Talk and written report)

Prüfungsleistung, Seminarleistung, Dauer (in Minuten): 20, benotet, 5 ECTS

Anteil an der Berechnung der Modulnote: 100.0 %

weitere Erläuterungen:
Talk (presenting paper/video) 20 min, written report 10-15 pages, valued 50% talk and 50% written report for grading

Prüfungssprache: Englisch

Erstablegung: SS 2022, 1. Wdh.: SS 2023

1. Prüfer:

Andreas Kist