Learning-based Localization with Monocular Camera for Light-rail System

Meng YAO; Kebin JIA; Wanchi SIU

doi:10.11999/JEIT171017

Volume 40 Issue 9

Aug. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(9): 2127-2134

Meng YAO, Kebin JIA, Wanchi SIU. Learning-based Localization with Monocular Camera for Light-rail System[J]. Journal of Electronics & Information Technology, 2018, 40(9): 2127-2134. doi: 10.11999/JEIT171017

Citation:

Meng YAO, Kebin JIA, Wanchi SIU. Learning-based Localization with Monocular Camera for Light-rail System[J]. Journal of Electronics & Information Technology, 2018, 40(9): 2127-2134. doi: 10.11999/JEIT171017

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Learning-based Localization with Monocular Camera for Light-rail System

doi: 10.11999/JEIT171017

Meng YAO^{1, 2},
Kebin JIA^{1, 3, 4
,
,},
Wanchi SIU²

1.
Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
2.
Department of Electronic and Information Engineering, Hong Kong Polytechnic University, Hong Kong, China
3.
Beijing Laboratory of Advanced Information Networks, Beijing 100124, China
4.
Advanced Innovation Center for Future Internet Technology, Beijing 100124, China

Funds: The National Natural Science Foundation of China (61672064), The Beijing Natural Science Foundation (KZ201610005007)

Received Date: 2017-10-31
Rev Recd Date: 2018-05-21

Available Online: 2018-07-12

Publish Date: 2018-09-01

Abstract

Abstract

The visual-based scene recognition and localization module is widely used in vehicle safety system. This paper proposes a new method of scene recognition based on local key region and key frame, which is based on the problem of large amount of training data, large matching complexity and low tracking precision. The proposed method meets the real-time requirements with high accuracy. First, the method uses the unsupervised method to extract the significant regions of the single reference sequence captured by the monocular camera as the key regions. The binary features with low correlation in key regions are also extracted to improve the scene matching accuracy and reduce the computational complexity of feature generation and matching. Secondly, key frames in the reference sequence are extracted based on the discrimination score to reduce the retrieval range of the tracking module and improve the efficiency. Practical field tests are done on real data of the light railway system in Hong Kong and the open test data set in Nordland. The experimental results show that the proposed method achieves fast matching and the precision is 9.8% higher than SeqSLAM which is based on global feature.
- Visual-based localization,
- Key region,
- Key frame,
- Binary feature

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References(24)

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