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3D object detection based on multi-view feature point matching
Author(s): Tian Yang; Xinzhu Sang; Duo Chen; Nan Guo; Peng Wang; Xunbo Yu; Binbin Yan; Kuiru Wang; Chongxiu Yu
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Paper Abstract

The result of object detection based on deep learning may have errors or omissions due to the occlusion and background in object detection, which is an intractable problem. An effective method of improving object detection performance using multiple viewpoint images are proposed. By performing feature point matching on objects in the overlap between different views, groups of points with semantic information can be obtained. These point groups can be used to generate new detection boxes, which can correct error ones in the raw results. Experiments show that the proposed method is a viable solution, the recall is significantly improved.

Paper Details

Date Published: 18 December 2019
PDF: 7 pages
Proc. SPIE 11342, AOPC 2019: AI in Optics and Photonics, 113420O (18 December 2019); doi: 10.1117/12.2548012
Show Author Affiliations
Tian Yang, Beijing Univ. of Posts and Telecommunications (China)
Xinzhu Sang, Beijing Univ. of Posts and Telecommunications (China)
Duo Chen, Beijing Univ. of Posts and Telecommunications (China)
Nan Guo, Beijing Univ. of Posts and Telecommunications (China)
Peng Wang, Beijing Univ. of Posts and Telecommunications (China)
Xunbo Yu, Beijing Univ. of Posts and Telecommunications (China)
Binbin Yan, Beijing Univ. of Posts and Telecommunications (China)
Kuiru Wang, Beijing Univ. of Posts and Telecommunications (China)
Chongxiu Yu, Beijing Univ. of Posts and Telecommunications (China)


Published in SPIE Proceedings Vol. 11342:
AOPC 2019: AI in Optics and Photonics
John Greivenkamp; Jun Tanida; Yadong Jiang; HaiMei Gong; Jin Lu; Dong Liu, Editor(s)

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