Analysis of object tracking algorithms performance on event-based datasets

Bachelor thesis (2022)

Authors

A.C. Olaru Electrical Engineering, Mathematics and Computer Science

Contributors

N. Tömen Pattern Recognition and Bioinformatics - EEMCS (supervisor 1)
O. Strafforello Pattern Recognition and Bioinformatics - EEMCS (supervisor 1)
X. Liu Pattern Recognition and Bioinformatics - EEMCS (supervisor 1)
L. Cavalcante Siebert Interactive Intelligence - EEMCS (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2022 Alexandra Olaru
Particle filter Clustering Event-based data Single object tracking Multi-target tracking
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Published Date

22-06-2022

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

The event-based camera represents a revolutionary concept, having an asynchronous output. The pixels of dynamic vision sensors react to the brightness change, resulting in streams of events at very small intervals of time. This paper provides a model to track objects in neuromorphic datasets, using clustering. In addition, a non-linear filter is applied to correct the estimation of the object position. Both single and multi-object tracking algorithms are provided and their performance is analyzed using different metrics, including the clustering evaluation scores and the tracking accuracy. The accuracy is over 0.6 for multi-target tracking and more than 0.7 for single object tracking. Besides the proposed model, a comparison between different possible approaches for event-based data tracking is provided.