ABSTRACT
We study the problem of tracking the movement of groups using sparse trajectory data extracted from Location Based Social Networks (LBSNs). Tracking group movement using LBSN data is challenging because the data may contain a large amount of noise due to the lack of stability in group entity, spatial extent and posting time. We propose a first-of-its-kind solution, Group Kalman Filter (GKF), which aims to improve the effectiveness of group tracking by predicting the spatial properties of groups with a group movement model. Our experiments with real LBSN data and synthetic LBSN data show that GKF can detect groups and predict group movement with a high level of accuracy and efficiency.
- Oleg Batrashev, Amnir Hadachi, Artjom Lind, and Eero Vainikko. 2015. Mobility episode detection from CDR's data using switching Kalman filter. In 4th ACM SIGSPATIAL, MobiGIS 2015. 63--69. Google ScholarDigital Library
- Alberto Belussi and Sara Migliorini. 2012. A framework for integrating multi-accuracy spatial data in geographical applications. GeoInformatica 16, 3 (2012), 523--561. Google ScholarDigital Library
- Robert Grover Brown and Patrick Y. C. Hwang. 2012. Canonical Problem: Localization and Tracking. In Introduction to Random Signals and Applied Kalman Filtering with Matlab Exercises, 4th Edition. John Wiley and Sons Inc., Chapter 4, 141--172.Google Scholar
- Junghoon Chae, Dennis Thom, Yun Jang, SungYe Kim, Thomas Ertl, and David S. Ebert. 2014. Public behavior response analysis in disaster events utilizing visual analytics of microblog data. Computers & Graphics 38 (2014), 51--60. Google ScholarDigital Library
- Xi Chen, Xiao Wang, and Jianhua Xuan. 2018. Tracking Multiple Moving Objects Using Unscented Kalman Filtering Techniques. CoRR abs/1802.01235 (2018).Google Scholar
- Rudolph Emil Kalman. 1960. A new approach to linear filtering and prediction problems. Journal of basic Engineering 82, 1 (1960), 35--45.Google ScholarCross Ref
- Panos Kalnis, Nikos Mamoulis, and Spiridon Bakiras. 2005. On Discovering Moving Clusters in Spatio-temporal Data. In SSTD (Lecture Notes in Computer Science, Vol. 3633). Springer, 364--381. Google ScholarDigital Library
- In-Soo Kang, Tae-wan Kim, and Ki-Joune Li. 1997. A Spatial Data Mining Method by Delaunay Triangulation. In GIS. ACM, 35--39. Google ScholarDigital Library
- Sameera Kannangara, Egemen Tanin, Aaron Harwood, and Shanika Karunasekera. 2018. Stepping stone graph for public movement analysis. In SIGSPATIAL. ACM, 149--158. Google ScholarDigital Library
- Jae-Gil Lee, Jiawei Han, and Kyu-Young Whang. 2007. Trajectory clustering: a partition-and-group framework. In SIGMOD. ACM, 593--604. Google ScholarDigital Library
- Xin Li, Kejun Wang, Wei Wang, and Yang Li. 2010. A multiple object tracking method using Kalman filter. In The 2010 IEEE International Conference on Information and Automation. 1862--1866.Google ScholarCross Ref
- Yiding Liu, Tuan-Anh Pham, Gao Cong, and Quan Yuan. 2017. An Experimental Evaluation of Point-of-interest Recommendation in Location-based Social Networks. VLDB 10, 10 (2017), 1010--1021. Google ScholarDigital Library
- Dipto Sarkar, Renée Sieber, and Raja Sengupta. 2016. GIScience Considerations in Spatial Social Networks. In GIScience (Lecture Notes in Computer Science, Vol. 9927). Springer, 85--98.Google ScholarCross Ref
- Bart Thomee, David A. Shamma, Gerald Friedland, Benjamin Elizalde, Karl Ni, Douglas Poland, Damian Borth, and Li-Jia Li. 2016. YFCC100M: the new data in multimedia research. Commun. ACM 59, 2 (2016), 64--73. Google ScholarDigital Library
- Hadi Fanaee Tork. 2012. Spatio-temporal clustering methods classification. In Doctoral Symposium on Informatics Engineering, Vol. 1. Faculdade de Engenharia da Universidade do Porto Porto, Portugal, 199--209.Google Scholar
- Wenlu Wang and Wei-Shinn Ku. 2016. Dynamic indoor navigation with Bayesian filters. SIGSPATIAL Special 8, 3 (2016), 9--10. Google ScholarDigital Library
- Martin Wirz, Pablo Schläpfer, Mikkel Baun Kjærgaard, Daniel Roggen, Sebastian Feese, and Gerhard Tröster. 2011. Towards an online detection of pedestrian flocks in urban canyons by smoothed spatio-temporal clustering of GPS trajectories. In LBSN. ACM, 17--24. Google ScholarDigital Library
- Shishan Yang and Marcus Baum. 2016. Second-order extended Kalman filter for extended object and group tracking. In FUSION. IEEE, 1178--1184.Google Scholar
- Yu Zheng, Lizhu Zhang, Xing Xie, and Wei-Ying Ma. 2009. Mining interesting locations and travel sequences from GPS trajectories. In WWW. ACM, 791--800. Google ScholarDigital Library
- Rui Zhou. 2016. Pedestrian dead reckoning on smartphones with varying walking speed. In ICC. IEEE, 1--6.Google Scholar
Index Terms
- Tracking Group Movement in Location Based Social Networks
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