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Detection of pedestrian actions based on deep learning approach

Danut Ovidiu Pop 1, 2, 3
Abstract : 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.
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https://hal.inria.fr/hal-02414015
Contributor : Anne Verroust-Blondet <>
Submitted on : Monday, December 16, 2019 - 2:22:41 PM
Last modification on : Thursday, January 16, 2020 - 10:44:03 AM
Long-term archiving on: : Tuesday, March 17, 2020 - 8:35:46 PM

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  • HAL Id : hal-02414015, version 1

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Danut Ovidiu Pop. Detection of pedestrian actions based on deep learning approach. Studia Universitatis Babeş-Bolyai. Informatica, Babeș-Bolyai University, 2019. ⟨hal-02414015⟩

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