A Finite State Machine Aided Pedestrian Navigation State Matching Algorithm

被引：0
|
作者：
Fang Z. [1 ]
Luo H. [2 ]
Li L. [1 ]
机构：
[1] State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan
[2] Second School in Surveying, Mapping and Geographic Information, Chengdu, 610100, Sichuan Province
来源：
Cehui Xuebao/Acta Geodaetica et Cartographica Sinica | 2017年 / 46卷 / 03期
基金：
中国国家自然科学基金;
关键词：
Action recognition; Finite state machine; Navigation state matching; Pedestrian navigation;
D O I：
10.11947/j.AGCS.2017.20160530
中图分类号：
学科分类号：
摘要：
The automatic identification of pedestrian's navigation state is a difficult problem in pedestrian navigation research. It is important to improve the precision feedback and navigation performance of pedestrian navigation services, and few researches have been done in this field. This paper proposes a pedestrian navigation state matching algorithm based on finite state machine (FSM). The main idea of this method is to identify the pedestrian navigation state on the basis of recognizing pedestrian's actions. The pedestrian's action characteristics are recognized by using multiple sensor data collected by Google glass and mobile phone. Then, the pedestrian navigation states are divided into familiar, unfamiliar and lost state. The state transition model is established according to the FSM theory, and the pedestrian navigation state matching algorithm based on the model is designed. Finally, this algorithm is implemented, and experiments are conducted to validate its effectiveness. Experimental results show that the proposed algorithm can reach a good precision of recognizing the state transitions during pedestrian navigation process, and especially the accuracy of recognizing lost state achieves 90%. © 2017, Surveying and Mapping Press. All right reserved.
引用
收藏
页码：371 / 380
页数：9
相关论文
共 25 条
  • [1] Zhang X., Li Q., Fang Z., Et al., Landmark and Branch-based Pedestrian Route Complexity and Selection Algorithm, Geomatics and Information Science of Wuhan University, 38, 10, pp. 1239-1242, (2013)
  • [2] Chen Y., Wu G., Chen F., Route Directions Method of Pedestrian Navigation Based on Landmark, Geography and Geo-Information Science, 31, 1, pp. 17-22, (2015)
  • [3] Li Q., Li Q., Fang Z., An Emergency Evacuation Routing Optimization Method Based on Space-time Congestion Concept, Acta Geodaetica et Cartographica Sinica, 40, 4, pp. 517-523, (2011)
  • [4] Yu H., Lu F., A Multi-modal Multi-criteria Route Planning Method Based on Genetic Algorithm, Acta Geodaetica et Cartographica Sinica, 43, 1, pp. 89-96, (2014)
  • [5] Fang Z., Li Q., Zhang X., A GIS Data Model for Landmark-based Pedestrian Navigation, International Journal of Geographical Information Science, 26, 5, pp. 817-838, (2012)
  • [6] Li R., Wang L., Wang K., A Survey of Human Body Action Recognition, Pattern Recognition and Artificial Intelligence, 27, 1, pp. 35-48, (2014)
  • [7] Ling Z., Zhao C., Liang Y., Et al., Survey on Vision-based Human Action Understanding, Application Research of Computers, 25, 9, pp. 2570-2578, (2008)
  • [8] Poppe R., A Survey on Vision-based Human Action Recognition, Image and Vision Computing, 28, 6, pp. 976-990, (2010)
  • [9] Weinland D., Ronfard R., Boyer E., A Survey of Vision-based Methods for Action Representation, Segmentation and Recognition, Computer Vision and Image Understanding, 115, 2, pp. 224-241, (2011)
  • [10] Lara O.D., Perez A.J., Labrador M.A., Et al., Centinela: A Human Activity Recognition System Based on Acceleration and Vital Sign Data, Pervasive and Mobile Computing, 8, 5, pp. 717-729, (2012)
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