Fast Joint Object Detection and Viewpoint Estimation for Traffic Scene Understanding

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dc.contributor.author Guindel Gómez, Carlos
dc.contributor.author Martín Gómez, David
dc.contributor.author Armingol Moreno, José María
dc.date.accessioned 2021-05-18T11:58:18Z
dc.date.available 2021-05-18T11:58:18Z
dc.date.issued 2018-09-28
dc.identifier.bibliographicCitation Guindel, C., Martin, D. & Armingol, J. M. (2018). Fast Joint Object Detection and Viewpoint Estimation for Traffic Scene Understanding. IEEE Intelligent Transportation Systems Magazine, 10(4), pp. 74–86.
dc.identifier.issn 1939-1390
dc.identifier.uri http://hdl.handle.net/10016/32669
dc.description.abstract Environment perception is a critical enabler for automated driving systems since it allows a comprehensive understanding of traffic situations, which is a requirement to ensure safe and reliable operation. Among the different applications, obstacle identification is a primary module of the perception system. We propose a vision-based method built upon a deep convolutional neural network that can reason simultaneously about the location of objects in the image and their orientations on the ground plane. The same set of convolutional layers is used for the different tasks involved, avoiding the repetition of computations over the same image. Experiments on the KITTI dataset show that our efficiency-oriented method achieves state-of-the-art accuracies for object detection and viewpoint estimation, and is particularly suitable for the recognition of traffic situations from on-board vision systems. Code is available at https://github.com/cguindel/Isi-faster-renn.
dc.format.extent 13
dc.language.iso eng
dc.publisher IEEE
dc.rights © 2018, IEEE
dc.subject.other Convolution
dc.subject.other Feedforward neural nets
dc.subject.other Object detection
dc.subject.other Object recognition
dc.subject.other Robot vision
dc.subject.other Traffic engineering computing
dc.title Fast Joint Object Detection and Viewpoint Estimation for Traffic Scene Understanding
dc.type article
dc.subject.eciencia Ingeniería Mecánica
dc.identifier.doi https://doi.org/10.1109/MITS.2018.2867526
dc.rights.accessRights openAccess
dc.relation.projectID Comunidad de Madrid. S2013/MIT-2713
dc.relation.projectID Gobierno de España. TRA2015-63708-R
dc.relation.projectID Gobierno de España. TRA2016-78886-C3-1-R
dc.type.version acceptedVersion
dc.identifier.publicationfirstpage 74
dc.identifier.publicationissue 4
dc.identifier.publicationlastpage 86
dc.identifier.publicationtitle IEEE Intelligent Transportation Systems Magazine
dc.identifier.publicationvolume 10
dc.identifier.uxxi AR/0000022276
dc.contributor.funder Ministerio de Economía y Competitividad (España)
dc.contributor.funder Comunidad de Madrid
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