Indoor Pedestrian Mobile Activity Recognition and Trajectory Tracking

XIONG Hanjiang; GUO Sheng; ZHENG Xianwei; ZHOU Yan

doi:10.13203/j.whugis20170066

Volume 43 Issue 11

Nov. 2018

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Geomatics and Information Science of Wuhan University > 2018 > 43(11): 1696-1703. > DOI: 10.13203/j.whugis20170066

XIONG Hanjiang, GUO Sheng, ZHENG Xianwei, ZHOU Yan. Indoor Pedestrian Mobile Activity Recognition and Trajectory Tracking[J]. Geomatics and Information Science of Wuhan University, 2018, 43(11): 1696-1703. DOI: 10.13203/j.whugis20170066

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Indoor Pedestrian Mobile Activity Recognition and Trajectory Tracking

1.
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

Funds:

The National Key Research and Development Program of China 2016YFB0502203

Mapping Geographic Information Industry Research Projects of Public Interest Industry 201512009

the Special Research Funding of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing

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Author Bio:
XIONG Hanjiang, PhD, professor, specializes in 3D GIS and indoor GIS.E-mail:xionghanjiang@163.com
Corresponding author:
ZHENG Xianwei, PhD.E-mail:zhengxw@whu.edu.cn
Received Date: September 06, 2017
Published Date: November 04, 2018

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Abstract

Abstract

As the basis of indoor location services, indoor localization technology has received more and more attention in recent years. Aiming at the problems of high cost, limited precision and insufficient efficiency in existing indoor positioning technologies, pedestrian dead reckoning (PDR), human acti-vity recognition (HAR) and landmarks are combined to obtain more accurate pedestrian indoor localization. PDR is used to estimate the user's location, and the cumulative error of PDR is reduced by landmarks, which are sensed by HAR. In addition, to solve the initial position determination problem, a hidden Markov model that considers the characteristics of the indoor environment is applied to match the continuous trajectory. The experimental results show that the proposed method has a good performance in activity recognition and positioning accuracy, and can track the user's trajectory efficiently.
- mobile activity recognition,
- indoor localization,
- trajectory tracking

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References (30)

References

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