Tiny Drone Tracking Framework Using Multiple Trackers and Kalman-based Predictor

Authors

DOI:

https://doi.org/10.13052/jwe1540-9589.2088

Keywords:

Object Tracking, Unmanned Aerial Vehicles, Drones, Surveillance System

Abstract

Unmanned aerial vehicles like drones are one of the key development technologies with many beneficial applications. As they have made great progress, security and privacy issues are also growing. Drone tacking with a moving camera is one of the important methods to solve these issues. There are various challenges of drone tracking. First, drones move quickly and are usually tiny. Second, images captured by a moving camera have illumination changes. Moreover, the tracking should be performed in real-time for surveillance applications. For fast and accurate drone tracking, this paper proposes a tracking framework utilizing two trackers, a predictor, and a refinement process. One tracker finds a moving target based on motion flow and the other tracker locates the region of interest (ROI) employing histogram features. The predictor estimates the trajectory of the target by using a Kalman filter. The predictor contributes to keeping track of the target even if the trackers fail. Lastly, the refinement process decides the location of the target taking advantage of ROIs from the trackers and the predictor. In experiments on our dataset containing tiny flying drones, the proposed method achieved an average success rate of 1.134 times higher than conventional tracking methods and it performed at an average run-time of 21.08 frames per second.

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Author Biographies

Sohee Son, Hanbat National University, South Korea

Sohee Son received her B.S. and M.S. degrees from Hanbat National University, Daejeon, Korea, in 2015 and 2017, respectively, from the department of multimedia engineering. Currently, she is working toward Ph.D. degree in the department of multimedia engineering in Hanbat National University. Her research interests include video coding, parallel processing, and computer vision.

Jeongin Kwon, Hanbat National University, South Korea

Jeongin Kwon received a B.S degree in industrial management engineering and a M.S degree in multimedia engineering from Hanbat National University, Daejeon, Korea, in 2014 and 2020, respectively. Currently, she is working on a personal project on deep learning. Her research interests include computer vision, image processing and deep learning.

Hui-Yong Kim, Kyung Hee University, South Korea

Hui-Yong Kim received his B.S., M.S., and Ph.D. degrees from Korea Advanced Institute of Science and Technology (KAIST) in 1994, 1998, and 2004, respectively. He is currently an Associate Professor in the Dept. of Computer Engineering in Kyung Hee University, Yongin, Korea since Mar. 2020. He was also an Associate Professor in the Dept. of Electronics Engineering in Sookmyung Women’s University, Seoul, Korea from Sep. 2019 to Feb. 2020. From 2005 to 2019, prior to joining universities, we worked for Electronics and Telecommunications Research Institute (ETRI), Daejeon, Korea as the Managing Director of Realistic Audio & Video Research Group. From 2003 to 2005, he worked for AddPac Technology Co. Ltd., Seoul as the leader of Multimedia Research Team. He was also an affiliate professor in University of Science and Technology (UST) and was a visiting scholar in the Media Communications Lab. at University of Southern California (USC), USA. His current research interest includes video signal processing and compression for realistic media applications such as UHD, 3D, VR, HDR, and Hologram.

Haechul Choi, Hanbat National University, South Korea

Haechul Choi received his B.S. in electronics engineering from Kyungpook National University, Daegu, Korea, in 1997, and his M.S. and Ph.D. in electrical engineering from the Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea, in 1999 and 2004, respectively. He is a professor in Information and Communication Engineering at Hanbat National University, Daejeon, Korea. From 2004 to 2010, he was a Senior Member of the Research Staff in the Broadcasting Media Research Group of the Electronics and Telecommunications Research Institute (ETRI). His current research interests include image processing, video coding, and video transmission.

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Published

2021-11-21

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