Please use this identifier to cite or link to this item: https://doi.org/10.20965/jrm.2021.p0329
Title: Small flying object classifications based on trajectories and support vector machines
Authors: Chan, JJX
Srigrarom, S 
Cao, J 
Wang, P 
Ratsamee, P
Issue Date: 1-Jan-2021
Publisher: Fuji Technology Press Ltd.
Citation: Chan, JJX, Srigrarom, S, Cao, J, Wang, P, Ratsamee, P (2021-01-01). Small flying object classifications based on trajectories and support vector machines. Journal of Robotics and Mechatronics 33 (2) : 329-338. ScholarBank@NUS Repository. https://doi.org/10.20965/jrm.2021.p0329
Abstract: This paper presents an alternative approach to identify and classify the group of small flying objects especially drones from others, notably birds and kites (inclusive of kiteflying), in near field, by examining the pattern of their flight paths and trajectories. The trajectories of the drones and other flying objects were extracted from multiple clips of videos including various natural and synthetic database. Four trajectories characteristics are observed and extracted from the object’s flight paths, i.e., heading or turning angle, curvature, pace velocity, and pace acceleration. Subsequently, principal component analyses were applied to reduce the number of these trajectory features from 4 to 2 parameters. Multi-class classification by support vector machine (SVM) with non-linear transformation kernel was used. Multiple classification models were developed by several algorithms with various transformation kernels. The hyperparameters were optimized using Bayesian optimization. The performances of the different models are compared through the prediction accuracy of the test data.
Source Title: Journal of Robotics and Mechatronics
URI: https://scholarbank.nus.edu.sg/handle/10635/217736
ISSN: 09153942
18838049
DOI: 10.20965/jrm.2021.p0329
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