Abstract
Unstable object tracking usually happens in hand-held video sequences that consist of the movements of both tracked object and camera. The tracked objects in these sequences are highly unpredictable in displacement and appearance changes, and create more challenge than tracking with a static camera. In this paper, we propose a two-mode tracking model for dealing with unstable situations, such as scaling, pose changes, and abrupt movements. The short-range tracking mode is for the scaling and appearance changes. This mode incorporates Lukas and Kanade’s optical flow and the CAMShift, in which to achieve high accuracy, both color and corner point features are fused. The long rangetracking mode utilizes particle filter and CAMShift to capture fast and abrupt motion. We design a mode selection strategy based on a failure detection method for adaptation with each tracking case. The proposed tracking model shows high performance with difficult sequences against the recent tracking systems, as well as achieving real-time processing on smart phones.
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Vo Quang Nhat received his B.S. degree in Information Technology from the University of Science Ho Chi Minh City, Vietnam in 2010, and his M.S. degree in Electronics and Computer Engineering from the Chonnam National University, Korea in 2013. He is currently a Ph.D. student in the department of Chonnam National University, Korea. His interesting studies are in multimedia and image processing, vision tracking, and pattern recognition.
Soo-Hyung Kim received his B.S. degree in Computer Engineering from Seoul National University in 1986, and his M.S. and Ph.D. degrees in Computer Science from Korea Advanced Institute of Science and Technology, in 1988 and 1993, respectively. From 1990 to 1996, he was a senior member of research staff in Multimedia Research Center of Samsung Electronics Co., Korea. Since 1997, he has been a professor in the Department of Computer Science, Chonnam National University, Korea. His research interests are pattern recognition, document image processing, medical image processing, and ubiquitous computing
Hyung Jeong Yang received her B.S., M.S. and Ph.D. from Chonbuk National University, Korea. She is currently an associate professor at the Deprtment. of Electronics and Computer Engineering, Chonnam National University, Gwangju, Korea. Her main research interests include multimedia data mining, pattern recognition, artificial intelligence, e-Learning, and e-Design.
Gueesang Lee received his B.S. degree in Electrical Engineering and his M.S. degree in Computer Engineering from Seoul National University, Korea, in 1980 and 1982, respectively. He received his Ph.D. degree in Computer Science from Pennsylvania State University in 1991. He is currently a professor of the Department of Electronics and Computer Engineering in Chonnam National University, Korea. His research interests are mainly in the field of image processing, computer vision and video technology.
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Nhat, V.Q., Kim, SH., Yang, H.J. et al. Real-time face tracking with instability using a feature-based adaptive model. Int. J. Control Autom. Syst. 13, 725–732 (2015). https://doi.org/10.1007/s12555-014-0126-0
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DOI: https://doi.org/10.1007/s12555-014-0126-0