JACIII Vol.27 No.3 pp. 386-393
doi: 10.20965/jaciii.2023.p0386

Research Paper:

Exploring Self-Attention for Visual Intersection Classification

Haruki Nakata, Kanji Tanaka, and Koji Takeda

Graduate School of Engineering, University of Fukui
3-9-1 Bunkyo, Fukui 910-8507, Japan

July 9, 2022
January 10, 2023
May 20, 2023
visual intersection classification, non-local contexts, self-attention, first-person-view net, third-person-view net

Self-attention has recently emerged as a technique for capturing non-local contexts in robot vision. This study introduced a self-attention mechanism into an intersection recognition system to capture non-local contexts behind the scenes. This mechanism is effective in intersection classification because most parts of the local pattern (e.g., road edges, buildings, and sky) are similar; thus, the use of a non-local context (e.g., the angle between two diagonal corners around an intersection) would be effective. This study makes three major contributions to existing literature. First, we proposed a self-attention-based approach for intersection classification. Second, we integrated the self-attention-based classifier into a unified intersection classification framework to improve the overall recognition performance. Finally, experiments using the public KITTI dataset showed that the proposed self-attention-based system outperforms conventional recognition based on local patterns and recognition based on convolution operations.

Self-attention for enhancing third person views

Self-attention for enhancing third person views

Cite this article as:
H. Nakata, K. Tanaka, and K. Takeda, “Exploring Self-Attention for Visual Intersection Classification,” J. Adv. Comput. Intell. Intell. Inform., Vol.27 No.3, pp. 386-393, 2023.
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