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2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2016)
Shenzhen, China
Dec. 15, 2016 to Dec. 18, 2016
ISBN: 978-1-5090-1612-9
pp: 516-519
Shuo Hong Wang , School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, China
Xiang Liu , School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, China
Jingwen Zhao , School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, China
Ye Liu , College of Automation, Nanjing University of Posts and Telecommunications, Jiangsu, China
Yan Qiu Chen , School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, China
ABSTRACT
3D motion data of fish school is more valuable than 2D data for behavior and other researches. This paper proposes to use a master view tracking first strategy based on a novel master-slave camera setup. On this basis, fish are firstly tracked in master view in 2D after being extracted via an eye-focused Gaussian Mixture Model (E-GMM) detector. Then 3D trajectories are reconstructed by associating 2D tracking results in master view and detection results in slave views after fish in slave views are localized using an eye-focused Gabor (E-Gabor) detector. Experiments on data sets with different fish densities demonstrate that the proposed method outperforms two state-of-the-art methods in terms of 5 evaluation metrics.
INDEX TERMS
Three-dimensional displays, Two dimensional displays, Head, Tracking, Cameras, Trajectory, Detectors
CITATION

Shuo Hong Wang, Xiang Liu, Jingwen Zhao, Y. Liu and Y. Q. Chen, "3D tracking swimming fish school using a master view tracking first strategy," 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Shenzhen, China, 2016, pp. 516-519.
doi:10.1109/BIBM.2016.7822572
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