Citation

Kataoka H, Satoh Y, Aoki Y, Oikawa S, Matsui Y. Sensors (Basel) 2018; 18(2): s18020627.

Affiliation

National Traffic Safety and Environment Laboratory, Tokyo 182-0012, Japan. ymatsui@ntsel.go.jp.

Copyright

(Copyright © 2018, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/s18020627

PMID

29461473

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.

Language: en

Keywords

advanced driver-assistance systems (ADAS); driving recorder; fine-grained pedestrian action recognition; two-stream convnets