Detection of Pedestrian Actions Based on Deep Learning Approach

  • D. Pop Department of Computer Science, Faculty of Mathematics and Computer Science, Babes-Bolyai University, Cluj-Napoca, Romania


The pedestrian detection has attracted considerable attention from research due to its vast applicability in the field of autonomous vehicles.  In the last decade, various investigations were made to find an optimal solution to detect the pedestrians, but less of them were focused on detecting and recognition the pedestrian's action. In this paper, we converge on both issues:  pedestrian detection and pedestrian action recognize at the current detection time  (T=0)  based on the JAAD dataset, employing deep learning approaches. We propose a pedestrian detection component based on Faster R-CNN able to detect the pedestrian and also recognize if the pedestrian is crossing the street in the detecting time. The method is in contrast with the commonly pedestrian detection systems, which only discriminate between pedestrians and non-pedestrians among other road users.

Author Biography

D. Pop, Department of Computer Science, Faculty of Mathematics and Computer Science, Babes-Bolyai University, Cluj-Napoca, Romania

(1) Babeș-Bolyai University, Department of Computer Science, 1 M. Kogălniceanu Street, 400084 Cluj-Napoca, Romania
(2) INRIA Paris, RITS team, 2 Rue Simone IFF, 75012 Paris, France
(3) INSA Rouen, LITIS, 685 Avenue de l'Université, 76800 Saint-Étienne-du-Rouvray, France


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How to Cite
POP, D.. Detection of Pedestrian Actions Based on Deep Learning Approach. Studia Universitatis Babeș-Bolyai Informatica, [S.l.], v. 64, n. 2, p. 5-13, aug. 2019. ISSN 2065-9601. Available at: <>. Date accessed: 29 nov. 2020. doi: