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OmniDRL: Robust Pedestrian Detection using Deep Reinforcement Learning on Omnidirectional Cameras
Univ Lisbon, Inst Super Tecn, Inst Syst & Robot LARSyS, Lisbon, Portugal..
Univ Lisbon, Inst Super Tecn, Inst Syst & Robot LARSyS, Lisbon, Portugal..
Univ Lisbon, Inst Super Tecn, Inst Syst & Robot LARSyS, Lisbon, Portugal..
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).ORCID iD: 0000-0002-8551-2448
2019 (English)In: 2019 International Conference on Robotics and Automation (ICRA) / [ed] Howard, A Althoefer, K Arai, F Arrichiello, F Caputo, B Castellanos, J Hauser, K Isler, V Kim, J Liu, H Oh, P Santos, V Scaramuzza, D Ude, A Voyles, R Yamane, K Okamura, A, Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 4782-4789Conference paper, Published paper (Refereed)
Abstract [en]

Pedestrian detection is one of the most explored topics in computer vision and robotics. The use of deep learning methods allowed the development of new and highly competitive algorithms. Deep Reinforcement Learning has proved to be within the state-of-the-art in terms of both detection in perspective cameras and robotics applications. However, for detection in omnidirectional cameras, the literature is still scarce, mostly because of their high levels of distortion. This paper presents a novel and efficient technique for robust pedestrian detection in omnidirectional images. The proposed method uses deep Reinforcement Learning that takes advantage of the distortion in the image. By considering the 3D bounding boxes and their distorted projections into the image, our method is able to provide the pedestrian's position in the world, in contrast to the image positions provided by most state-of-the-art methods for perspective cameras. Our method avoids the need of preprocessing steps to remove the distortion, which is computationally expensive. Beyond the novel solution, our method compares favorably with the state-of-the-art methodologies that do not consider the underlying distortion for the detection task.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2019. p. 4782-4789
Series
IEEE International Conference on Robotics and Automation ICRA, ISSN 1050-4729
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-265465DOI: 10.1109/ICRA.2019.8794471ISI: 000494942303068Scopus ID: 2-s2.0-85071489274ISBN: 978-1-5386-6026-3 (print)OAI: oai:DiVA.org:kth-265465DiVA, id: diva2:1380057
Conference
2019 International Conference on Robotics and Automation, ICRA 2019; Palais des Congres de Montreal, Montreal; Canada; 20-24 May 2019
Note

QC 20191218

Available from: 2019-12-18 Created: 2019-12-18 Last updated: 2019-12-20Bibliographically approved

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Dos Santos Miraldo, Pedro

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