Is the pedestrian going to cross? : answering by 2D pose estimation
Fang, Zhijie (Universitat Autònoma de Barcelona. Departament de Ciències de la Computació)
López Peña, Antonio M. (Centre de Visió per Computador (Bellaterra, Catalunya))

Publicació:	Institute of Electrical and Electronics Engineers (IEEE), cop.2018
Descripció:	6 pàg.
Resum:	Our recent work suggests that, thanks to nowadays powerful CNNs, image-based 2D pose estimation is a promising cue for determining pedestrian intentions such as crossing the road in the path of the ego-vehicle, stopping before entering the road, and starting to walk or bending towards the road. This statement is based on the results obtained on non-naturalistic sequences (Daimler dataset), i. e. in sequences choreographed specifically for performing the study. Fortunately, a new publicly available dataset (JAAD) has appeared recently to allow developing methods for detecting pedestrian intentions in naturalistic driving conditions; more specifically, for addressing the relevant question is the pedestrian going to cross? Accordingly, in this paper we use JAAD to assess the usefulness of 2D pose estimation for answering such a question. We combine CNN-based pedestrian detection, tracking and pose estimation to predict the crossing action from monocular images. Overall, the proposed pipeline provides new state-of-the-art results.
Ajuts:	Agencia Estatal de Investigación TIN2017-88709-R
Nota:	Altres ajuts: the authors want to acknowledge the Spanish DGT project SPIP2017-02237 and the Generalitat de Catalunya CERCA Program and its ACCIO agency
Drets:	Tots els drets reservats.
Llengua:	Anglès
Document:	Capítol de llibre ; recerca ; Versió acceptada per publicar
Matèria:	Task analysis ; Pose estimation ; Skeleton ; Two dimensional displays ; Legged locomotion ; Roads ; Feature extraction
Publicat a:	2018 IEEE Intelligent Vehicles Symposium (IV), 2018, p. 1271-1276, ISBN 978-1-5386-4452-2

DOI: 10.1109/IVS.2018.8500413

Postprint 7 p, 9.7 MB

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Llibres i col·leccions > Capítols de llibres