A Fast $O(N)$ Multiresolution Polygonal Approximation Algorithm for GPS Trajectory Simplification

Recent advances in geopositioning mobile phones have made it possible for users to collect a large number of GPS trajectories by recording their location information. However, these mobile phones with built-in GPS devices usually record far more data than needed, which brings about both heavy data storage and a computationally expensive burden in the rendering process for a Web browser. To address this practical problem, we present a fast polygonal approximation algorithm in 2-D space for the GPS trajectory simplification under the so-called integral square synchronous distance error criterion in a linear time complexity. The underlying algorithm is designed and implemented using a bottom-up multiresolution method, where the input of polygonal approximation in the coarser resolution is the polygonal curve achieved in the finer resolution. For each resolution (map scale), priority-queue structure is exploited in graph construction to construct the initialized approximated curve. Once the polygonal curve is initialized, two fine-tune algorithms are employed in order to achieve the desirable quality level. Experimental results validated that the proposed algorithm is fast and achieves a better approximation result than the existing competitive methods.

[1]  Pasi Fränti,et al.  A fast near-optimal min-# polygonal approximation of digitized curves , 2002 .

[2]  Barbara P. Buttenfield,et al.  Transmitting Vector Geospatial Data across the Internet , 2002, GIScience.

[3]  John Hershberger,et al.  Cartographic Line Simplification and Polygon CSG Formulae and in O(n log* n) Time , 1997, WADS.

[4]  Bimal Kumar Ray,et al.  A non-parametric sequential method for polygonal approximation of digital curves , 1994, Pattern Recognit. Lett..

[5]  Duncan A. Buell,et al.  Splash 2 - FPGAs in a custom computing machine , 1996 .

[6]  George K. Papakonstantinou,et al.  Parallel approaches to piecewise linear approximation , 1994, Signal Process..

[7]  Kuo-Liang Chung,et al.  Novel efficient two-pass algorithm for closed polygonal approximation based on LISE and curvature constraint criteria , 2008, J. Vis. Commun. Image Represent..

[8]  Ovidiu Daescu New Results on Path Approximation , 2003, Algorithmica.

[9]  Juan Carlos Pérez-Cortes,et al.  Optimum polygonal approximation of digitized curves , 1994, Pattern Recognit. Lett..

[10]  Touradj Ebrahimi,et al.  Progressive Content-Based Shape Compression for Retrieval of Binary Images , 1998, Comput. Vis. Image Underst..

[11]  Alexander Wolff,et al.  Farthest-point queries with geometric and combinatorial constraints , 2004, Comput. Geom..

[12]  Pasi Fränti,et al.  Compression of GPS Trajectories , 2012, 2012 Data Compression Conference.

[13]  Alexander Kolesnikov,et al.  Multiresolution polygonal approximation of digital curves , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[14]  Ovidiu Daescu,et al.  Polygonal chain approximation: a query based approach , 2005, Comput. Geom..

[15]  Marc Salotti,et al.  Optimal polygonal approximation of digitized curves using the sum of square deviations criterion , 2002, Pattern Recognit..

[16]  Yu Zheng,et al.  Computing with Spatial Trajectories , 2011, Computing with Spatial Trajectories.

[17]  Alexander Kolesnikov,et al.  Reduced-search dynamic programming for approximation of polygonal curves , 2003, Pattern Recognit. Lett..

[18]  Eugene Bodansky,et al.  A New Method of Polyline Approximation , 2004, SSPR/SPR.

[19]  Marc Salotti An efficient algorithm for the optimal polygonal approximation of digitized curves , 2001, Pattern Recognit. Lett..

[20]  Franz Aurenhammer,et al.  Handbook of Computational Geometry , 2000 .

[21]  Ouri Wolfson,et al.  Spatio-temporal data reduction with deterministic error bounds , 2003, DIALM-POMC '03.

[22]  M. Iri,et al.  Polygonal Approximations of a Curve — Formulations and Algorithms , 1988 .

[23]  S. S. Ravi,et al.  SQUISH: an online approach for GPS trajectory compression , 2011, COM.Geo.

[24]  Nanbor Wang,et al.  Distributed component support for integrating large-scale parallel HPC applications , 2007, CompFrame '07.

[25]  Alexander Kolesnikov Fast algorithm for ISE-bounded polygonal approximation , 2008, 2008 15th IEEE International Conference on Image Processing.

[26]  Jack Dongarra,et al.  Basic Linear Algebra Subprograms (BLAS) , 2011, Encyclopedia of Parallel Computing.

[27]  Pankaj K. Agarwal,et al.  Efficient Algorithms for Approximating Polygonal Chains , 2000, Discret. Comput. Geom..

[28]  J. P. Gray,et al.  The case for custom computation , 1991 .

[29]  Kuo-Liang Chung,et al.  Efficient algorithms for 3-D polygonal approximation based on LISE criterion , 2002, Pattern Recognit..

[30]  Pierre-François Marteau,et al.  Speeding up simplification of polygonal curves using nested approximations , 2007, Pattern Analysis and Applications.

[31]  Helmut Alt,et al.  Matching Polygonal Curves with Respect to the Fréchet Distance , 2001, STACS.

[32]  Its'hak Dinstein,et al.  An algorithm for polygonal approximation based on iterative point elimination , 1995, Pattern Recognit. Lett..

[33]  W. S. Chan,et al.  Approximation of Polygonal Curves with Minimum Number of Line Segments or Minimum error , 1996, Int. J. Comput. Geom. Appl..

[34]  Godfried T. Toussaint,et al.  On Approximating Polygonal Curves in Two and Three Dimensions , 1994, CVGIP Graph. Model. Image Process..

[35]  Paul L. Rosin Assessing the Behaviour of Polygonal Approximation Algorithms , 1998, BMVC.

[36]  Nirvana Meratnia,et al.  Spatiotemporal Compression Techniques for Moving Point Objects , 2004, EDBT.

[37]  S. S. Ravi,et al.  Algorithms for compressing GPS trajectory data: an empirical evaluation , 2010, GIS '10.

[38]  Hui Ding,et al.  Querying and mining of time series data: experimental comparison of representations and distance measures , 2008, Proc. VLDB Endow..

[39]  Timos K. Sellis,et al.  Sampling Trajectory Streams with Spatiotemporal Criteria , 2006, 18th International Conference on Scientific and Statistical Database Management (SSDBM'06).

[40]  Paul L. Rosin Techniques for Assessing Polygonal Approximations of Curves , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[41]  Joseph S. B. Mitchell,et al.  Simplifying a polygonal subdivision while keeping it simple , 2001, SCG '01.

[42]  Ovidiu Daescu,et al.  Space-Efficient Algorithms for Approximating Polygonal Curves in Two Dimensional Space , 1998, COCOON.

[43]  S. Chiba,et al.  Dynamic programming algorithm optimization for spoken word recognition , 1978 .

[44]  John Hershberger,et al.  Cartographic line simplification and polygon CSG formulæ in O(nlog * n) time , 1998, Comput. Geom..

[45]  Paul L. Rosin Assessing the behaviour of polygonal approximation algorithms , 2003, Pattern Recognit..

[46]  A. Melkman,et al.  On Polygonal Chain Approximation , 1988 .

[47]  Nenghai Yu,et al.  Trajectory simplification method for location-based social networking services , 2009, LBSN '09.

[48]  Frank Dürr,et al.  Remote real-time trajectory simplification , 2009, 2009 IEEE International Conference on Pervasive Computing and Communications.

[49]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[50]  David H. Douglas,et al.  ALGORITHMS FOR THE REDUCTION OF THE NUMBER OF POINTS REQUIRED TO REPRESENT A DIGITIZED LINE OR ITS CARICATURE , 1973 .