Convolutional neural network for detection and classification with event-based data

Damien Joubert, Konik Hubert, Chausse Frederic

Research output: Chapter in Book / Conference PaperConference Paperpeer-review

3 Citations (Scopus)

Abstract

![CDATA[Mainly inspired by biological perception systems, event-based sensors provide data with many advantages such as timing precision, data compression and low energy consumption. In this work, it is analyzed how these data can be used to detect and classify cars, in the case of front camera automotive applications. The basic idea is to merge state of the art deep learning algorithms with event-based data integrated into artificial frames. When this preprocessing method is used in viewing purposes, it suggests that the shape of the targets can be extracted, but only when the relative speed is high enough between the camera and the targets. Event-based sensors seems to provide a more robust description of the target’s trajectory than using conventional frames, the object only being described by its moving edges, and independently of lighting conditions. It is also highlighted how features trained on conventional greylevel images can be transferred to event-based data to efficiently detect car into pseudo images.]]
Original languageEnglish
Title of host publicationVISIGRAPP 2019: Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (Volume 5), 25 - 27 February, 2019, Prague, Czech Republic
PublisherSciTePress
Pages200-208
Number of pages9
ISBN (Print)9789897583544
DOIs
Publication statusPublished - 2019
EventVISIGRAPP (Conference) -
Duration: 25 Feb 2019 → …

Conference

ConferenceVISIGRAPP (Conference)
Period25/02/19 → …

Keywords

  • motor vehicles
  • neural networks (computer science)
  • optical detectors

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