An intelligent indoor positioning system based on pedestrian directional signage object detection: a case study of Taipei Main Station

Chun-Chao Yeh; Ke-Jia Jhang; Chin-Chun Chang; Chun-Chao Yeh; Ke-Jia Jhang; Chin-Chun Chang

doi:10.3934/mbe.2020015

Mathematical Biosciences and Engineering

2020, Volume 17, Issue 1: 266-285. doi: 10.3934/mbe.2020015

Previous Article Next Article

Research article Special Issues

An intelligent indoor positioning system based on pedestrian directional signage object detection: a case study of Taipei Main Station

Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung 20224, Taiwan

Received: 30 June 2019 Accepted: 25 September 2019 Published: 08 October 2019

Indoor positioning technologies have gained great interest from both industry and academia. Variety of services and applications can be built based on the availability and accessibility of indoor positioning information, for example indoor navigation and various location-based services. Different approaches have been proposed to provide indoor positioning information to users, in which an underlying system infrastructure is usually assumed to be well deployed in advance to provide the position information to users. Among many others, one common strategy is to deploy a bunch of active sensor nodes, such as WiFi APs and Bluetooth transceivers, to the indoor environment to serve as reference landmarks. The user's current location can thus be obtained directly or indirectly according to the active sensor signals collected by the user. Different from conventional infrastructure-based approaches, which put additional sensor devices to the environment, we utilize available objects in the environment as location landmarks. Leveraging wildly available smartphone devices as customer premises equipment to the user and the cutting-edge deep-learning technology, we investigate the feasibility of an infrastructure-free intelligent indoor positioning system based on visual information only. The proposed scheme has been verified by a real case study, which is to provide indoor positioning information to users in Taipei Main Station, one of the busiest transportation stations in the world. We use available pedestrian directional signage as location landmarks, which include all of the 52 pedestrian directional signs in the testing area. The Google Objection Detection framework is applied for detection and recognition of the pedestrian directional sign. According to the experimental results, we have shown that the proposed scheme can achieve as high as 98% accuracy to successfully identify the 52 pedestrian directional signs for the three test data sets which include 6,341 test images totally. Detailed discussions of the system design and the experiments are also presented in the paper.
- indoor positioning,
- signage detection,
- deep-learning,
- R-CNN,
- smart environment
Citation: Chun-Chao Yeh, Ke-Jia Jhang, Chin-Chun Chang. An intelligent indoor positioning system based on pedestrian directional signage object detection: a case study of Taipei Main Station[J]. Mathematical Biosciences and Engineering, 2020, 17(1): 266-285. doi: 10.3934/mbe.2020015

Related Papers:

Abstract

Indoor positioning technologies have gained great interest from both industry and academia. Variety of services and applications can be built based on the availability and accessibility of indoor positioning information, for example indoor navigation and various location-based services. Different approaches have been proposed to provide indoor positioning information to users, in which an underlying system infrastructure is usually assumed to be well deployed in advance to provide the position information to users. Among many others, one common strategy is to deploy a bunch of active sensor nodes, such as WiFi APs and Bluetooth transceivers, to the indoor environment to serve as reference landmarks. The user's current location can thus be obtained directly or indirectly according to the active sensor signals collected by the user. Different from conventional infrastructure-based approaches, which put additional sensor devices to the environment, we utilize available objects in the environment as location landmarks. Leveraging wildly available smartphone devices as customer premises equipment to the user and the cutting-edge deep-learning technology, we investigate the feasibility of an infrastructure-free intelligent indoor positioning system based on visual information only. The proposed scheme has been verified by a real case study, which is to provide indoor positioning information to users in Taipei Main Station, one of the busiest transportation stations in the world. We use available pedestrian directional signage as location landmarks, which include all of the 52 pedestrian directional signs in the testing area. The Google Objection Detection framework is applied for detection and recognition of the pedestrian directional sign. According to the experimental results, we have shown that the proposed scheme can achieve as high as 98% accuracy to successfully identify the 52 pedestrian directional signs for the three test data sets which include 6,341 test images totally. Detailed discussions of the system design and the experiments are also presented in the paper.

References

[1]	K. Wang, X. Yu, Q. Xiong, et al., Learning to improve WLAN indoor positioning accuracy based on DBSCAN-KRF algorithm from RSS fingerprint data, IEEE Access, 7 (2019), 72308-72315.
[2]	L. Chen, B. Li, K. Zhao, et al., An improved algorithm to generate a Wi-Fi fingerprint database for indoor positioning,Sensors, 13 (2013), 11085-11096.
[3]	C. H. Lin, L. H. Chen, H. K. Wu, et al., An indoor positioning algorithm based on fingerprint and mobility prediction in RSS fluctuation-prone WLANs, IEEE T. Syst. Man. Cy. S, 6 (2019), 1-11 (Early Access).
[4]	Md. S. Ifthekhar, N. Saha and Y. M. Jang, Neural network based indoor positioning technique in optical camera communication system, in Proceedings of 2014 International Conference on Indoor Positioning and Indoor Navigation (IPIN), IEEE Comput. Soc., (2014), 431-435.
[5]	G. Schroeer, A real-time UWB multi-channel indoor positioning system for industrial scenarios, in Proceedings of 2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN), IEEE Comput. Soc., (2018), 1-5.
[6]	X. Gan, B. Yu, Z. Heng, et al., Indoor combination positioning technology of Pseudolites and PDR, in Proceedings of 2018 Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS), IEEE Comput. Soc., (2018), 1-7.
[7]	J. Huang, V. Rathod, C. Sun, et al., Speed/accuracy trade-os for modern convolutional object detectors, in Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE Comput. Soc., (2017), 3296-3297.
[8]	R. Girshick, J. Donahue, T. Darrell, et al., Rich feature hierarchies for accu-rate object detection and semantic segmentation, in Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE Comput. Soc., (2014), 580-587.
[9]	S. Ren, K. He, R. Girshick, et al., Faster R-CNN: Towards real-time object detection with region proposal networks, IEEE T. Pattern Anal., 39 (2017), 1137-1149.
[10]	J. Dai, Y. Li, K. He, et al., R-FCN: object detection via region-based fully convolutional networks, preprint,CoRR(2016), arXiv:1605.06409.
[11]	J. R. R. Uijlings, K. E. A. van de Sande, T. Gevers, et al., Selective search for object recognition, Int. J. Comput. Vision, 104 (2013), 154-171.
[12]	J. Redmon, S. K. Divvala, R. B. Girshick, et al., You only look once: Unified, real-time object detection, CoRR(2015), arXiv:1506.02640.
[13]	W. Liu, D. Anguelov, D. Erhan, et al., SSD: Single shot multibox detector, in Leibe B., Matas J., Sebe N., Welling M. (eds) Computer Vision-ECCV 2016. Lect. Notes Comput. Sc.,9905 (2016), 21-37.
[14]	T. Lin, P. Goyal, R. Girshick, et al., Focal loss for dense object detection, in Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), IEEE Comput. Soc., (2017), 2999-3007.
[15]	YOLO: Real-Time Object Detection. Available from: https://pjreddie.com/darknet/yolo/.
[16]	GitHub, tensorflow/models. Available from: https://github.com/tensorflow/models/tree/master/research/object_detection.
[17]	Taipei Main Station, Wikipedia. Available from: https://en.wikipedia.org/wiki/Taipei_Main_Station.
[18]	Statistics inquiry, Ministry of transportation and Communications, Taiwan. Available from: http://stat.motc.gov.tw/mocdb/stmain.jsp?sys=100&funid=emenu.
[19]	London Waterloo station, Wikipedia, Available from: https://en.wikipedia.org/wiki/London_Waterloo_station.

Reader Comments

Your name:*

Email:*
© 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)