TY - GEN
T1 - Data Pre-processing Based on Convolutional Neural Network for Improving Precision of Indoor Positioning
AU - Lu, Eric Hsueh Chan
AU - Chang, Kuei Hua
AU - Ciou, Jing Mei
N1 - Funding Information:
This research was supported by Ministry of Science and Technology, Taiwan, R.O.C. under grant no. MOST 108-2621-M-006-008-.
Funding Information:
Acknowledgment. This research was supported by Ministry of Science and Technology, Taiwan, R.O.C. under grant no. MOST 108-2621-M-006-008 -.
Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - In the past, indoor positioning technology was mainly based on pedestrian dead reckoning and wireless signal positioning methods, but it was easy to cause some problems such as error accumulation and signal interference. Positioning accuracy still needs to be improved. With the development of neural networks in recent years, many researchers have successfully applied the neural network to the indoor positioning problem based on the Convolutional Neural Network (CNN). This technique mainly determines the position of the image by matching the image features. CNN faces the same challenges as other supervised learning. If the “clean” data cannot be collected, the trained model will not achieve good positioning accuracy. For CNN used for indoor positioning, if someone passes through in the training data, causing the person to appear in different positions of the images, the model may think that the images are the same location. To solve this problem, we propose a data pre-processing method to improve the accuracy of indoor positioning based on CNN. In this method, the moving objects recognized in training and testing data are modified in different ways. We perform data pre-processing method based on Mask R-CNN and YOLO, and then integrate the pre-processing method to PoseNet the famous CNN indoor positioning architecture. Through real experimental analysis, removing moving objects can effectively improve indoor positioning accuracy about 46%.
AB - In the past, indoor positioning technology was mainly based on pedestrian dead reckoning and wireless signal positioning methods, but it was easy to cause some problems such as error accumulation and signal interference. Positioning accuracy still needs to be improved. With the development of neural networks in recent years, many researchers have successfully applied the neural network to the indoor positioning problem based on the Convolutional Neural Network (CNN). This technique mainly determines the position of the image by matching the image features. CNN faces the same challenges as other supervised learning. If the “clean” data cannot be collected, the trained model will not achieve good positioning accuracy. For CNN used for indoor positioning, if someone passes through in the training data, causing the person to appear in different positions of the images, the model may think that the images are the same location. To solve this problem, we propose a data pre-processing method to improve the accuracy of indoor positioning based on CNN. In this method, the moving objects recognized in training and testing data are modified in different ways. We perform data pre-processing method based on Mask R-CNN and YOLO, and then integrate the pre-processing method to PoseNet the famous CNN indoor positioning architecture. Through real experimental analysis, removing moving objects can effectively improve indoor positioning accuracy about 46%.
UR - http://www.scopus.com/inward/record.url?scp=85082303701&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85082303701&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-41964-6_47
DO - 10.1007/978-3-030-41964-6_47
M3 - Conference contribution
AN - SCOPUS:85082303701
SN - 9783030419639
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 545
EP - 552
BT - Intelligent Information and Database Systems - 12th Asian Conference, ACIIDS 2020, Proceedings
A2 - Nguyen, Ngoc Thanh
A2 - Trawinski, Bogdan
A2 - Jearanaitanakij, Kietikul
A2 - Chittayasothorn, Suphamit
A2 - Selamat, Ali
PB - Springer
T2 - 12th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2020
Y2 - 23 March 2020 through 26 March 2020
ER -