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Three-dimensional object tracking based on perspective scale invariant feature transform correspondences

[+] Author Affiliations
Wei Chen

South China University of Technology, School of Computer Science and Engineering, Guangzhou, China

GuangDong Pharmaceutical University, College of Medical Information Engineering, Guangzhou, China

Luming Liang

Colorado School of Mines, Department of Electrical Engineering and Computer Science, Golden, Colorado, United States

Yuelong Zhao

South China University of Technology, School of Computer Science and Engineering, Guangzhou, China

Shu Chen

Xiangtan University, School of Information Engineering, Xiangtan, China

Xiangtan University, Key Laboratory of Intelligent Computing and Information Processing, Ministry of Education, Xiangtan, China

J. Electron. Imaging. 26(3), 033022 (Jun 12, 2017). doi:10.1117/1.JEI.26.3.033022
History: Received January 6, 2017; Accepted May 18, 2017
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Abstract.  Reconstructing three-dimensional (3-D) poses from matched feature correspondences is widely used in 3-D object tracking. The precision of correspondence matching plays a major role in the pose reconstruction. Without prior knowledge of the perspective camera model, state-of-the-art methods only deal with two-dimensional (2-D) planar affine transforms. An interest point’s detector and descriptor [perspective scale invariant feature transform (SIFT)] is proposed to overcome the side effects of viewpoint changing, i.e., our detector is invariant to viewpoint changing. Perspective SIFT is detected by the SIFT approach, where the sample region is determined by projecting the original sample region to the image plane based on the established camera model. An iterative algorithm then modifies the pose of the tracked object and it generally converges to a 3-D perspective invariant point. The pose of the tracked object is finally estimated by the combination of template warping and perspective SIFT correspondences. Thorough evaluations are performed on two public databases, the Biwi Head Pose dataset and the Boston University dataset. Comparisons illustrate that the proposed keypoint’s detector largely improves the tracking performance.

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Citation

Wei Chen ; Luming Liang ; Yuelong Zhao and Shu Chen
"Three-dimensional object tracking based on perspective scale invariant feature transform correspondences", J. Electron. Imaging. 26(3), 033022 (Jun 12, 2017). ; http://dx.doi.org/10.1117/1.JEI.26.3.033022


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