TY - GEN
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
KW - distance estimation
KW - image processing
KW - walking speed estimation
UR - http://www.scopus.com/inward/record.url?scp=85099347957&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85099347957&partnerID=8YFLogxK
U2 - 10.1109/GCCE50665.2020.9291915
DO - 10.1109/GCCE50665.2020.9291915
M3 - Conference contribution
AN - SCOPUS:85099347957
T3 - 2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020
SP - 276
EP - 277
BT - 2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th IEEE Global Conference on Consumer Electronics, GCCE 2020
Y2 - 13 October 2020 through 16 October 2020
ER -