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Prediction of Driver's Visual Attention in Critical Moment Using Optical Flow

Rebeka SULTANA, Gosuke OHASHI

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Summary :

In recent years, driver's visual attention has been actively studied for driving automation technology. However, the number of models is few to perceive an insight understanding of driver's attention in various moments. All attention models process multi-level image representations by a two-stream/multi-stream network, increasing the computational cost due to an increment of model parameters. However, multi-level image representation such as optical flow plays a vital role in tasks involving videos. Therefore, to reduce the computational cost of a two-stream network and use multi-level image representation, this work proposes a single stream driver's visual attention model for a critical situation. The experiment was conducted using a publicly available critical driving dataset named BDD-A. Qualitative results confirm the effectiveness of the proposed model. Moreover, quantitative results highlight that the proposed model outperforms state-of-the-art visual attention models according to CC and SIM. Extensive ablation studies verify the presence of optical flow in the model, the position of optical flow in the spatial network, the convolution layers to process optical flow, and the computational cost compared to a two-stream model.

Publication
IEICE TRANSACTIONS on Information Vol.E106-D No.5 pp.1018-1026
Publication Date
2023/05/01
Publicized
2023/01/26
Online ISSN
1745-1361
DOI
10.1587/transinf.2022EDP7146
Type of Manuscript
PAPER
Category
Artificial Intelligence, Data Mining

Authors

Rebeka SULTANA
  Shizuoka University
Gosuke OHASHI
  Shizuoka University

Keyword