Deep Pedestrian Distance Estimation from Single-shot Image

Kazuki Murayama; Kenji Kanai; Masaru Takeuchi; Jiro Katto

doi:10.1109/GCCE50665.2020.9291915

Deep Pedestrian Distance Estimation from Single-shot Image

Kazuki Murayama, Kenji Kanai, Masaru Takeuchi, Jiro Katto

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

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.

Original language	English
Title of host publication	2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	276-277
Number of pages	2
ISBN (Electronic)	9781728198026
DOIs	https://doi.org/10.1109/GCCE50665.2020.9291915
Publication status	Published - 2020 Oct 13
Event	9th IEEE Global Conference on Consumer Electronics, GCCE 2020 - Kobe, Japan Duration: 2020 Oct 13 → 2020 Oct 16

Publication series

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

Conference

Conference	9th IEEE Global Conference on Consumer Electronics, GCCE 2020
Country/Territory	Japan
City	Kobe
Period	20/10/13 → 20/10/16

Keywords

deep learning
distance estimation
image processing
walking speed estimation

ASJC Scopus subject areas

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

Access to Document

10.1109/GCCE50665.2020.9291915

Cite this

Murayama, K., Kanai, K., Takeuchi, M., & Katto, J. (2020). Deep Pedestrian Distance Estimation from Single-shot Image. In 2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020 (pp. 276-277). [9291915] (2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GCCE50665.2020.9291915

Deep Pedestrian Distance Estimation from Single-shot Image. / Murayama, Kazuki; Kanai, Kenji; Takeuchi, Masaru et al.
2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 276-277 9291915 (2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Murayama, K, Kanai, K, Takeuchi, M & Katto, J 2020, Deep Pedestrian Distance Estimation from Single-shot Image. in 2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020., 9291915, 2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020, Institute of Electrical and Electronics Engineers Inc., pp. 276-277, 9th IEEE Global Conference on Consumer Electronics, GCCE 2020, Kobe, Japan, 20/10/13. https://doi.org/10.1109/GCCE50665.2020.9291915

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title = "Deep Pedestrian Distance Estimation from Single-shot Image",

abstract = "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. ",

keywords = "deep learning, distance estimation, image processing, walking speed estimation",

author = "Kazuki Murayama and Kenji Kanai and Masaru Takeuchi and Jiro Katto",

note = "Funding Information: This paper is supported by the verification-style research & development program for solving reginal challenges using data cooperation and utilization by NIcT, Japan and partially supported by the R&D contract {"}Wired-and-Wireless Converged Radio Access Network for Massive IoT Traffic{"} with the Ministry of Internal Affairs and communications, Japan, for radio resource enhancement. Publisher Copyright: {\textcopyright} 2020 IEEE.; 9th IEEE Global Conference on Consumer Electronics, GCCE 2020 ; Conference date: 13-10-2020 Through 16-10-2020",

year = "2020",

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day = "13",

doi = "10.1109/GCCE50665.2020.9291915",

language = "English",

series = "2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020",

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T1 - Deep Pedestrian Distance Estimation from Single-shot Image

AU - Murayama, Kazuki

AU - Kanai, Kenji

AU - Takeuchi, Masaru

AU - Katto, Jiro

N1 - Funding Information: This paper is supported by the verification-style research & development program for solving reginal challenges using data cooperation and utilization by NIcT, Japan and partially supported by the R&D contract "Wired-and-Wireless Converged Radio Access Network for Massive IoT Traffic" with the Ministry of Internal Affairs and communications, Japan, for radio resource enhancement. Publisher Copyright: © 2020 IEEE.

PY - 2020/10/13

Y1 - 2020/10/13

N2 - 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.

AB - 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.

KW - deep learning

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KW - image processing

KW - walking speed estimation

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M3 - Conference contribution

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T3 - 2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020

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BT - 2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020

PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 13 October 2020 through 16 October 2020

ER -

Deep Pedestrian Distance Estimation from Single-shot Image

Abstract

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Conference

Keywords

ASJC Scopus subject areas

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