TY - GEN
T1 - Vision-based drones tracking using correlation filters and linear integrated multiple model
AU - Sie, Niven Junliang
AU - Xuan Seah, Shao
AU - Chan, Jalvin Jiaxiang
AU - Yi, Jiahe
AU - Chew, Kim Hoe
AU - Hooi Chan, Teng
AU - Srigrarom, Sutthiphong
AU - Holzapfel, Florian
AU - Hesse, Henrik
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/5/19
Y1 - 2021/5/19
N2 - This paper presents the use of Correlation Filters and Integrated Multiple Model (IMM) for filtering the position measurement of fast moving drones acquired by computer vision, with probability for model selection. The maneuvering movement of the drones are often non-linear making it hard to be estimated by a simple Kalman filter. Instead of using a non-linear filter which are more complex and non-universal, this paper attempt to integrate multiple filters to estimate the drone position using low computational cost. The IMM switches between the Constant Velocity (CV), Constant Acceleration (CA) and a Constant Turn (CT) model with a Markov Chain in different flight scenario depending on the drone movement. Other filters i.e. Kernelized Correlation Filter (KCF), Particle Filter (PF) and Discriminative Correlation Filter (DCF) models are also presented for direct comparison.
AB - This paper presents the use of Correlation Filters and Integrated Multiple Model (IMM) for filtering the position measurement of fast moving drones acquired by computer vision, with probability for model selection. The maneuvering movement of the drones are often non-linear making it hard to be estimated by a simple Kalman filter. Instead of using a non-linear filter which are more complex and non-universal, this paper attempt to integrate multiple filters to estimate the drone position using low computational cost. The IMM switches between the Constant Velocity (CV), Constant Acceleration (CA) and a Constant Turn (CT) model with a Markov Chain in different flight scenario depending on the drone movement. Other filters i.e. Kernelized Correlation Filter (KCF), Particle Filter (PF) and Discriminative Correlation Filter (DCF) models are also presented for direct comparison.
UR - http://www.scopus.com/inward/record.url?scp=85112805697&partnerID=8YFLogxK
U2 - 10.1109/ECTI-CON51831.2021.9454735
DO - 10.1109/ECTI-CON51831.2021.9454735
M3 - Conference contribution
AN - SCOPUS:85112805697
T3 - ECTI-CON 2021 - 2021 18th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology: Smart Electrical System and Technology, Proceedings
SP - 1085
EP - 1090
BT - ECTI-CON 2021 - 2021 18th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology
A2 - Kumsuwan, Yuttana
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2021
Y2 - 19 May 2021 through 22 May 2021
ER -