Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (11A): 283-289.doi: 10.11896/jsjkx.210200145

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Road Surface Object Detection from Mobile Phone Based Sensor Trajectories

JIAO Dong-lai1,2, WANG Hao-xiang3, LYU Hai-yang1,2, XU Ke1,2   

  1. 1 Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province,Nanjing University of Posts and Telecommunications,Nanjing 210023,China
    2 School of Geography and Bioinformatics,Nanjing University of Posts and Telecommunications,Nanjing 210023,China
    3 School of Communication and Information Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210023,China
  • Online:2021-11-10 Published:2021-11-12
  • About author:JIAO Dong-lai,born in 1977,Ph.D,associate professor.His main research interests include GIS and application of Internet of things.
  • Supported by:
    National Science Foundation of China(41471329).

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Abstract: Aimed at the problem of low efficiency and high cost in the traditional road surface object collection procedure,the method of road surface object recognition from mobile phone based sensor trajectories is proposed.Mobile phones are used to record the data changes of various sensors in the process of driving,and then the acceleration data after attitude correction are analyzed to find the relationship between the acceleration trend and the road condition.Finally,the constructe the BP neural network model,and use the acquired data to train the BP neural network model to recognize the road surface object and its position.Experiment results show that,the road surface object can be fast and accurately recognized by the mobile phone based senor trajectories,and the accuracy can be higher than 85%.In this paper,attitude of mobile acceleration sensor has carried on the real time correction.Because the acceleration changing of the Mobile phones is perpendicular to the road,we use the acceleration change to detecte the road feature.The method has nothing to do with a mobile phone accelerometer gesture,in addition,hardware requirements of the method are low,theefficiency of data acquisition is high,which reduce the cost of the road surface features information acquisition.

Key words: BP neural network, Mobile phone sensors, Road surface object recognition, Vehicle trajectories

CLC Number: 

  • P228
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