Trabajo presentado en la 31st British Machine Vision Conference, celebrada online del 7 al 10 de septiembre de 2020 ; Event cameras are bioinspired sensors with reaction times in the order of microseconds. This property makes them appealing for use in highly-dynamic computer vision applications. In this work, we explore the limits of this sensing technology and present an ultra-fast tracking algorithm able to estimate six-degree-of-freedom motion with dynamics over 25.8g, at a throughput of 10kHz, processing over a million events per second. Our method is capable of tracking either camera motion or the motion of an object in front of it, using an error-state Kalman filter formulated in a Lie-theoretic sense. The method includes a robust mechanism for the matching of events with projected line segments with very fast outlier rejection. Meticulous treatment of sparse matrices is applied to achieve real-time performance. Different motion models of varying complexity are considered for the sake of comparison and performance analysis. ; This work was partially supported by the EU H2020 project GAUSS (H2020-Galileo-2017- 1-776293), by the Spanish State Research Agency through projects EB-SLAM (DPI2017- 89564-P) and the María de Maeztu Seal of Excellence to IRI (MDM-2016-0656, and by a scholarship from SENESCYT, Republic of Ecuador to William Chamorro.
High-speed event camera tracking
2020-09-07
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
DDC: | 629 |
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