Deep Pedestrian Distance Estimation from Single-shot Image

Kazuki Murayama, Kenji Kanai, Masaru Takeuchi, Jiro Katto

研究成果: Conference contribution

抄録

In this paper, we propose a deep learning-based distance estimation method from a single-shot image. In the proposal, we model the estimation as a regression problem, and estimate the distance between a pedestrian and a camera by using three main features; size of bounding box, image blur and image features. By using KITTI dataset, we evaluate the accuracy of the proposed model.

本文言語English
ホスト出版物のタイトル2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020
出版社Institute of Electrical and Electronics Engineers Inc.
ページ276-277
ページ数2
ISBN(電子版)9781728198026
DOI
出版ステータスPublished - 2020 10 13
イベント9th IEEE Global Conference on Consumer Electronics, GCCE 2020 - Kobe, Japan
継続期間: 2020 10 132020 10 16

出版物シリーズ

名前2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020

Conference

Conference9th IEEE Global Conference on Consumer Electronics, GCCE 2020
CountryJapan
CityKobe
Period20/10/1320/10/16

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Media Technology
  • Instrumentation
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition

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