Small Flying Object Classifications Based on Trajectories and Support Vector Machines
Jalvin Jia Xiang Chan*, Sutthiphong Srigrarom*, Jiawei Cao**, Pengfei Wang**, and Photchara Ratsamee***
*Department of Mechanical Engineering, National University of Singapore
9 Engineering Drive 1, Block EA #07-08 117575, Singapore
**Temasek Laboratories, National University of Singapore
T-Lab Building, 5A Engineering Drive 1, #09-02 117411, Singapore
***Cyber Media Center, Osaka University
5-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan
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.
-  X. Shi, C. Yang, W. Xie, C. Liang, Z. Shi, and J. Chen, “Anti-drone system with multiple surveillance technologies: Architecture, implementation, and challenges,” IEEE Communications Magazine, Vol.56, No.4, pp. 68-74, 2018.
-  I. Guvenc, F. Koohifar, S. Singh, M. L. Sichitiu, and D. Matolak, “Detection, tracking, and interdiction for amateur drones” IEEE Communications Magazine, Vol.56, No.4, pp. 75-81, 2018.
-  M. M. Azari, H. Sallouha, A. Chiumento, S. Rajendran, E. Vinogradov, and S. Pollin, “Key technologies and system trade-offs for detection and localization of amateur drones,” IEEE Communications Magazine, Vol.56, No.1, pp. 51-57, 2018.
-  A. Coluccia, A. Fascista, A. Schumann, L. Sommer, M. Ghenescu, T. Piatrik, G. De Cubber, M. Nalamati, A. Kapoor, M. Saqib, N. Sharma, M. Blumenstein, V. Magoulianitis, D. Ataloglou, A. Dimou, D. Zarpalas, P. Daras, C. Craye, S. Ardjoune, D. De la Iglesia, M. Mández, R. Dosil, and I. González, “Drone-vs-bird detection challenge at IEEE AVSS2019,” 2019 16th IEEE Int. Conf. on Advanced Video and Signal Based Surveillance (AVSS), pp. 1-7, 2019.
-  A. Rozantsev, V. Lepetit, and P. Fua, “Flying objects detection from a single moving camera,” Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 4128-4136, 2015.
-  F. Gökçe, G. Üçoluk, E. Şahin, and S. Kalkan, “Vision-based detection and distance estimation of micro unmanned aerial vehicles,” Sensors, Vol.15, No.9, pp. 23805-23846, 2015.
-  R. Yoshihashi, T. T. Trinh, R. Kawakami, S. You, M. Iida, and T. Naemura, “Differentiating objects by motion: joint detection and tracking of small flying objects,” arXiv preprint, arXiv:1709.04666, 2017.
-  C. Aker and S. Kalkan, “Using deep networks for drone detection,” 2017 14th IEEE Int. Conf. on Advanced Video and Signal Based Surveillance (AVSS), pp. 1-6, 2017.
-  M. Saqib, S. D. Khan, N. Sharma, and M. Blumenstein, “A study on detecting drones using deep convolutional neural networks,” 2017 14th IEEE Int. Conf. on Advanced Video and Signal Based Surveillance (AVSS), pp. 1-5, 2017.
-  D. H. Ye, J. Li, Q. Chen, J. Wachs, and C. Bouman, “Deep learning for moving object detection and tracking from a single camera in unmanned aerial vehicles (UAVs),” Electronic Imaging, Vol.2018, No.10, pp. 466-1-466-6, 2018.
-  A. Schumann, L. Sommer, J. Klatte, T. Schuchert, and J. Beyerer, “Deep cross-domain flying object classification for robust UAV detection,” 2017 14th IEEE Int. Conf. on Advanced Video and Signal Based Surveillance (AVSS), pp. 1-6, 2017.
-  S. Srigrarom, K. H. Chew, D. M. Da Lee, and P. Ratsamee, “Drone versus bird flights: Classification by trajectories characterization,” Proc. of 2020 59th Annual Conf. of the Society of Instrument and Control Engineers of Japan (SICE), 2020.
-  V.-P. Thai, W. Zhong, T. Pham, S. Alam, and V. Duong, “Detection, tracking and classification of aircraft and drones in digital towers using machine learning on motion patterns,” 2019 Integrated Communications, Navigation and Surveillance Conf. (ICNS), pp. 1-8, 2019.
-  T. Alerstam, M. Rosén, J. Bäckman, P. G. P. Ericson, and O. Hellgren, “Flight speeds among bird species: allometric and phylogenetic effects,” PLoS Biol., Vol.5, No.8, e197, 2007.
-  J. De Leeuw, “Nonlinear principal component analysis and related techniques,” UCLA Department of Statistics Papers, 2011.
-  I. T. Jolliffe, “Principal component analysis,” Springer, 2002.
-  C. M. Bishop, “Pattern recognition and machine learning,” Springer, 2006.
-  K. Crammer and Y. Singer, “On the algorithmic implementation of multiclass kernel-based vector machines,” J. of Machine Learning Research, Vol.2, pp. 265-292, 2001.
-  A. J. Smola and B. Schölkopf, “A tutorial on support vector regression,” Statistics and Computing, Vol.14, No.3, pp. 199-222, 2004.
This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.