Estimating the UAVs' crash point based on optical flows' voting in the image plane

Yusaku Hatano; Hiroshi Nagahashi; Chen Yi; Jun Ohya

doi:10.2352/ISSN.2470-1173.2018.15.COIMG-402

Estimating the UAVs' crash point based on optical flows' voting in the image plane

Yusaku Hatano^*, Hiroshi Nagahashi, Chen Yi, Jun Ohya

^*Corresponding author for this work

Research output: Contribution to journal › Conference article › peer-review

Abstract

Towards the actualization of an air bag system for the UAV's crash, this paper proposes a method for estimating the UAV's crash site from the video sequence acquired by the camera attached to the UAV. The crash point can be considered to correspond to the divergence point of the optical flows. In the accumulator, the cells at which the optical flows (straight lines) pass through are incremented by one. After performing this process for all the optical flows, the cell with the largest vote is obtained as the crash point (divergence point) in the image plane. Experiments using a hand held camera show that the accuracy of estimating the crash site is increased as the camera approaches the target plane. Overall, the experimental results are promising.

Original language	English
Article number	S25
Journal	IS and T International Symposium on Electronic Imaging Science and Technology
Volume	Part F138654
DOIs	https://doi.org/10.2352/ISSN.2470-1173.2018.15.COIMG-402
Publication status	Published - 2018 Jan 1
Event	16th Computational Imaging Conference, COMIG 2018 - Burlingame, United States Duration: 2018 Jan 28 → 2018 Feb 1

ASJC Scopus subject areas

Computer Graphics and Computer-Aided Design
Computer Science Applications
Human-Computer Interaction
Software
Electrical and Electronic Engineering
Atomic and Molecular Physics, and Optics

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10.2352/ISSN.2470-1173.2018.15.COIMG-402

Cite this

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title = "Estimating the UAVs' crash point based on optical flows' voting in the image plane",

abstract = "Towards the actualization of an air bag system for the UAV's crash, this paper proposes a method for estimating the UAV's crash site from the video sequence acquired by the camera attached to the UAV. The crash point can be considered to correspond to the divergence point of the optical flows. In the accumulator, the cells at which the optical flows (straight lines) pass through are incremented by one. After performing this process for all the optical flows, the cell with the largest vote is obtained as the crash point (divergence point) in the image plane. Experiments using a hand held camera show that the accuracy of estimating the crash site is increased as the camera approaches the target plane. Overall, the experimental results are promising.",

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AU - Hatano, Yusaku

AU - Nagahashi, Hiroshi

AU - Yi, Chen

AU - Ohya, Jun

PY - 2018/1/1

Y1 - 2018/1/1

N2 - Towards the actualization of an air bag system for the UAV's crash, this paper proposes a method for estimating the UAV's crash site from the video sequence acquired by the camera attached to the UAV. The crash point can be considered to correspond to the divergence point of the optical flows. In the accumulator, the cells at which the optical flows (straight lines) pass through are incremented by one. After performing this process for all the optical flows, the cell with the largest vote is obtained as the crash point (divergence point) in the image plane. Experiments using a hand held camera show that the accuracy of estimating the crash site is increased as the camera approaches the target plane. Overall, the experimental results are promising.

AB - Towards the actualization of an air bag system for the UAV's crash, this paper proposes a method for estimating the UAV's crash site from the video sequence acquired by the camera attached to the UAV. The crash point can be considered to correspond to the divergence point of the optical flows. In the accumulator, the cells at which the optical flows (straight lines) pass through are incremented by one. After performing this process for all the optical flows, the cell with the largest vote is obtained as the crash point (divergence point) in the image plane. Experiments using a hand held camera show that the accuracy of estimating the crash site is increased as the camera approaches the target plane. Overall, the experimental results are promising.

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JO - IS and T International Symposium on Electronic Imaging Science and Technology

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T2 - 16th Computational Imaging Conference, COMIG 2018

Y2 - 28 January 2018 through 1 February 2018

ER -

Estimating the UAVs' crash point based on optical flows' voting in the image plane

Abstract

ASJC Scopus subject areas

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