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Real-time on-board pedestrian detection using generic single-stage algorithms and on-road databases

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Real-time on-board pedestrian detection using generic single-stage algorithms and on-road databases

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Ortiz, V.; Del Tejo Catala, O.; Salvador Igual, I.; Perez-Cortes, J. (2020). Real-time on-board pedestrian detection using generic single-stage algorithms and on-road databases. International Journal of Advanced Robotic Systems. 17(5). https://doi.org/10.1177/1729881420929175

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/168157

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Title: Real-time on-board pedestrian detection using generic single-stage algorithms and on-road databases
Author: ORTIZ, V. del Tejo Catala, Omar Salvador Igual, Ismael Perez-Cortes, Juan-Carlos
UPV Unit: Universitat Politècnica de València. Departamento de Informática de Sistemas y Computadores - Departament d'Informàtica de Sistemes i Computadors
Issued date:
Abstract:
[EN] Pedestrian detection is a particular case of object detection that helps to reduce accidents in advanced driver-assistance systems and autonomous vehicles. It is not an easy task because of the variability of the ...[+]
Subjects: Object detection , Artificial intelligence , Machine learning , Convolutional neural networks , Resource-constrained hardware , One-stage detectors , Advanced driver-assistance systems , Vulnerable road users
Copyrigths: Reconocimiento (by)
Source:
International Journal of Advanced Robotic Systems. (issn: 1729-8806 )
DOI: 10.1177/1729881420929175
Publisher:
SAGE Publications
Publisher version: https://doi.org/10.1177/1729881420929175
Project ID:
info:eu-repo/grantAgreement/EC/H2020/783190/EU/Programmable Systems for Intelligence in Automobiles/
Thanks:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by PRYSTINE project which had received funding within the Electronic ...[+]
Type: Artículo

References

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Zhang, S., Benenson, R., Omran, M., Hosang, J., & Schiele, B. (2018). Towards Reaching Human Performance in Pedestrian Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4), 973-986. doi:10.1109/tpami.2017.2700460

Viola, P., Jones, M. J., & Snow, D. (2005). Detecting Pedestrians Using Patterns of Motion and Appearance. International Journal of Computer Vision, 63(2), 153-161. doi:10.1007/s11263-005-6644-8

Dollar, P., Appel, R., Belongie, S., & Perona, P. (2014). Fast Feature Pyramids for Object Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(8), 1532-1545. doi:10.1109/tpami.2014.2300479

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Enzweiler, M., & Gavrila, D. M. (2009). Monocular Pedestrian Detection: Survey and Experiments. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(12), 2179-2195. doi:10.1109/tpami.2008.260

He, K., Zhang, X., Ren, S., & Sun, J. (2015). Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(9), 1904-1916. doi:10.1109/tpami.2015.2389824

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