Print Email Facebook Twitter Analysis of object tracking algorithms performance on event-based datasets Title Analysis of object tracking algorithms performance on event-based datasets Author Olaru, Alexandra (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Pattern Recognition and Bioinformatics) Contributor Tömen, N. (mentor) Strafforello, O. (mentor) Liu, X. (mentor) Cavalcante Siebert, L. (graduation committee) Degree granting institution Delft University of Technology Programme Computer Science and Engineering Project CSE3000 Research Project Date 2022-06-22 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. Subject Event-based dataSingle object trackingparticle filterClusteringmulti-target tracking To reference this document use: http://resolver.tudelft.nl/uuid:ee0ee456-f1f4-4d47-9c06-b184bcbe47a5 Bibliographical note https://github.com/aolaru11/Object-tracking-using-event-based-camera The implementation of the object tracking model using event based camera Part of collection Student theses Document type bachelor thesis Rights © 2022 Alexandra Olaru Files PDF Research_Paper_CSE3000_ex ... _final.pdf 462.21 KB Close viewer /islandora/object/uuid:ee0ee456-f1f4-4d47-9c06-b184bcbe47a5/datastream/OBJ/view