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Sensors 2018, 18(2), 627; https://doi.org/10.3390/s18020627

Temporal and Fine-Grained Pedestrian Action Recognition on Driving Recorder Database

1
National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba 305-8560, Japan
2
Department of Electronics and Electrical Engineering, Keio University, Yokohama 223-8522, Japan
3
Tokyo Metropolitan University, Tokyo 192-0364, Japan
4
National Traffic Safety and Environment Laboratory, Tokyo 182-0012, Japan
*
Author to whom correspondence should be addressed.
Received: 5 January 2018 / Revised: 7 February 2018 / Accepted: 8 February 2018 / Published: 20 February 2018
(This article belongs to the Special Issue Sensors for Transportation)
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Abstract

The paper presents an emerging issue of fine-grained pedestrian action recognition that induces an advanced pre-crush safety to estimate a pedestrian intention in advance. The fine-grained pedestrian actions include visually slight differences (e.g., walking straight and crossing), which are difficult to distinguish from each other. It is believed that the fine-grained action recognition induces a pedestrian intention estimation for a helpful advanced driver-assistance systems (ADAS). The following difficulties have been studied to achieve a fine-grained and accurate pedestrian action recognition: (i) In order to analyze the fine-grained motion of a pedestrian appearance in the vehicle-mounted drive recorder, a method to describe subtle change of motion characteristics occurring in a short time is necessary; (ii) even when the background moves greatly due to the driving of the vehicle, it is necessary to detect changes in subtle motion of the pedestrian; (iii) the collection of large-scale fine-grained actions is very difficult, and therefore a relatively small database should be focused. We find out how to learn an effective recognition model with only a small-scale database. Here, we have thoroughly evaluated several types of configurations to explore an effective approach in fine-grained pedestrian action recognition without a large-scale database. Moreover, two different datasets have been collected in order to raise the issue. Finally, our proposal attained 91.01% on National Traffic Science and Environment Laboratory database (NTSEL) and 53.23% on the near-miss driving recorder database (NDRDB). The paper has improved +8.28% and +6.53% from baseline two-stream fusion convnets. View Full-Text
Keywords: fine-grained pedestrian action recognition; two-stream convnets; driving recorder; advanced driver-assistance systems (ADAS) fine-grained pedestrian action recognition; two-stream convnets; driving recorder; advanced driver-assistance systems (ADAS)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Kataoka, H.; Satoh, Y.; Aoki, Y.; Oikawa, S.; Matsui, Y. Temporal and Fine-Grained Pedestrian Action Recognition on Driving Recorder Database. Sensors 2018, 18, 627.

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