Small Flying Object Classifications Based on Trajectories and Support Vector Machines

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.