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Proceedings Paper

The selection of the optimal baseline in the front-view monocular vision system
Author(s): Bincheng Xiong; Jun Zhang; Daimeng Zhang; Xiaomao Liu; Jinwen Tian
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Paper Abstract

In the front-view monocular vision system, the accuracy of solving the depth field is related to the length of the inter-frame baseline and the accuracy of image matching result. In general, a longer length of the baseline can lead to a higher precision of solving the depth field. However, at the same time, the difference between the inter-frame images increases, which increases the difficulty in image matching and the decreases matching accuracy and at last may leads to the failure of solving the depth field. One of the usual practices is to use the tracking and matching method to improve the matching accuracy between images, but this algorithm is easy to cause matching drift between images with large interval, resulting in cumulative error in image matching, and finally the accuracy of solving the depth field is still very low. In this paper, we propose a depth field fusion algorithm based on the optimal length of the baseline. Firstly, we analyze the quantitative relationship between the accuracy of the depth field calculation and the length of the baseline between frames, and find the optimal length of the baseline by doing lots of experiments; secondly, we introduce the inverse depth filtering technique for sparse SLAM, and solve the depth field under the constraint of the optimal length of the baseline. By doing a large number of experiments, the results show that our algorithm can effectively eliminate the mismatch caused by image changes, and can still solve the depth field correctly in the large baseline scene. Our algorithm is superior to the traditional SFM algorithm in time and space complexity. The optimal baseline obtained by a large number of experiments plays a guiding role in the calculation of the depth field in front-view monocular.

Paper Details

Date Published: 8 March 2018
PDF: 7 pages
Proc. SPIE 10609, MIPPR 2017: Pattern Recognition and Computer Vision, 106090F (8 March 2018); doi: 10.1117/12.2283470
Show Author Affiliations
Bincheng Xiong, Huazhong Univ. of Science and Technology (China)
Jun Zhang, Huazhong Univ. of Science and Technology (China)
Daimeng Zhang, Univ. of Maryland, College Park (United States)
Xiaomao Liu, Huazhong Univ. of Science and Technology (China)
Jinwen Tian, Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 10609:
MIPPR 2017: Pattern Recognition and Computer Vision
Zhiguo Cao; Yuehuang Wang; Chao Cai, Editor(s)

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