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Evaluation of Event-Based Corner Detectors

Abstract : Bio-inspired Event-Based (EB) cameras are a promising new technology that outperforms standard frame-based cameras in extreme lighted and fast moving scenes. Already, a number of EB corner detection techniques have been developed; however, the performance of these EB corner detectors has only been evaluated based on a few author-selected criteria rather than on a unified common basis, as proposed here. Moreover, their experimental conditions are mainly limited to less interesting operational regions of the EB camera (on which frame-based cameras can also operate), and some of the criteria, by definition, could not distinguish if the detector had any systematic bias. In this paper, we evaluate five of the seven existing EB corner detectors on a public dataset including extreme illumination conditions that have not been investigated before. Moreover, this evaluation is the first of its kind in terms of analysing not only such a high number of detectors, but also applying a unified procedure for all. Contrary to previous assessments, we employed both the intensity and trajectory information within the public dataset rather than only one of them. We show that a rigorous comparison among EB detectors can be performed without tedious manual labelling and even with challenging acquisition conditions. This study thus proposes the first standard unified EB corner evaluation procedure, which will enable better understanding of the underlying mechanisms of EB cameras and can therefore lead to more efficient EB corner detection techniques.
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https://hal.archives-ouvertes.fr/hal-03132336
Contributor : Aymeric Histace Connect in order to contact the contributor
Submitted on : Friday, February 5, 2021 - 7:24:09 AM
Last modification on : Friday, August 5, 2022 - 2:46:00 PM

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Distributed under a Creative Commons CC0 - Public Domain Dedication 4.0 International License

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Özgün Yilmaz, Camille Simon-Chane, Aymeric Histace. Evaluation of Event-Based Corner Detectors. Journal of Imaging, 2021, 7 (2), pp.25. ⟨10.3390/jimaging7020025⟩. ⟨hal-03132336⟩

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