TY - GEN
T1 - Distance Estimation Between Moving Objects Using Monocular Camera
AU - Habibi, M. R.
AU - Irawan, Mohammad Isa
AU - Setiyono, Budi
N1 - Publisher Copyright:
© 2023 American Institute of Physics Inc.. All rights reserved.
PY - 2023/1/27
Y1 - 2023/1/27
N2 - Distance estimation between two or more objects is a crucial task in the computer vision research area. Moreover, in the era of COVID-19, it becomes an urgent issue as it can enable social distance preserved. Distance estimation could be done using stereo vision (stereoscopic photogammetry) but requires more complexity. In this paper, researchers show distance estimation is possible using only monocular vision. We propose a deep-learning based method, Mobilenet Single Shot Detector (MSSD), combined with Camera Calibration to detect objects and estimate the distance between them in the setting of monocular vision. To verify the robustness of the proposed method, we created a dataset video using a monocular camera. The experimental results showed the performance of the proposed method could estimate the distance properly using the recorded dataset.
AB - Distance estimation between two or more objects is a crucial task in the computer vision research area. Moreover, in the era of COVID-19, it becomes an urgent issue as it can enable social distance preserved. Distance estimation could be done using stereo vision (stereoscopic photogammetry) but requires more complexity. In this paper, researchers show distance estimation is possible using only monocular vision. We propose a deep-learning based method, Mobilenet Single Shot Detector (MSSD), combined with Camera Calibration to detect objects and estimate the distance between them in the setting of monocular vision. To verify the robustness of the proposed method, we created a dataset video using a monocular camera. The experimental results showed the performance of the proposed method could estimate the distance properly using the recorded dataset.
UR - http://www.scopus.com/inward/record.url?scp=85147291563&partnerID=8YFLogxK
U2 - 10.1063/5.0111106
DO - 10.1063/5.0111106
M3 - Conference contribution
AN - SCOPUS:85147291563
T3 - AIP Conference Proceedings
BT - 3rd International Conference on Science, Mathematics, Environment, and Education
A2 - Indriyanti, Nurma Yunita
A2 - Sari, Meida Wulan
PB - American Institute of Physics Inc.
T2 - 3rd International Conference on Science, Mathematics, Environment, and Education: Flexibility in Research and Innovation on Science, Mathematics, Environment, and Education for Sustainable Development, ICoSMEE 2021
Y2 - 27 July 2021 through 28 July 2021
ER -