A Hybrid Algorithm for Online Location Update using Feature Point Detection for Portable Devices

• Jibum Kim,
• Inbin Kim,
• Namgu Kwon,
• Heemin Park,
• Jinseok Chae,

Vol. 9, No. 2, February 27, 2015
10.3837/tiis.2015.02.007, Download Paper (Free):

• Location-based service
• Corner Detection
• Moving Object Tracking
• Online Trajectory Sampling

Abstract

We propose a cost-efficient hybrid algorithm for online location updates that efficiently combines feature point detection with the online trajectory-based sampling algorithm. Our algorithm is designed to minimize the average trajectory error with the minimal number of sample points. The algorithm is composed of 3 steps. First, we choose corner points from the map as sample points because they will most likely cause fewer trajectory errors. By employing the online trajectory sampling algorithm as the second step, our algorithm detects several missing and important sample points to prevent unwanted trajectory errors. The final step improves cost efficiency by eliminating redundant sample points on straight paths. We evaluate the proposed algorithm with real GPS trajectory data for various bus routes and compare our algorithm with the existing one. Simulation results show that our algorithm decreases the average trajectory error 28% compared to the existing one. In terms of cost efficiency, simulation results show that our algorithm is 29% more cost efficient than the existing one with real GPS trajectory data.

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