Research Article Open Access

Multiple Vehicle Tracking using Adaptive Gaussian Mixture Model and Kalman Filter

Fandy Setyo Utomo1
  • 1 STMIK AMIKOM Purwokerto, Indonesia
American Journal of Applied Sciences
Volume 13 No. 12, 2016, 1407-1412

DOI: https://doi.org/10.3844/ajassp.2016.1407.1412

Submitted On: 15 October 2016
Published On: 22 December 2016

How to Cite: Utomo, F. S. (2016). Multiple Vehicle Tracking using Adaptive Gaussian Mixture Model and Kalman Filter. American Journal of Applied Sciences, 13(12), 1407-1412. https://doi.org/10.3844/ajassp.2016.1407.1412

Abstract

Based on data from the Central Statistics Agency (BPS) of the Republic of Indonesia in catalog Crime Statistics in 2014, the number of crimes of theft of motor vehicles has increased since 2010 until 2013. Recorded in 2013 the number reached 42.508 cases of theft. In this study, we made a prototype application using Adaptive Gaussian Mixture Models algorithms and the Kalman Filter to detect the movement of the vehicle in order to prevent vehicle theft. Adaptive Gaussian Mixture Models were used for image segmentation foreground and background, while the Kalman Filter was used to track the vehicle. Stages in this study consisted of two phases, namely the manufacture of prototype and testing of prototype applications. Testing was done by observing the resource usage of memory (RAM) and the processor when the application was executed and the speed and degree of vehicle motion detection accuracy. The test results showed that the prototype application using Adaptive Gaussian Mixture Model and Kalman Filter had an accuracy rate of 90% and a high speed in detecting motion with the use of vehicles under 65000 KB RAM and processor work load below 27% on condition vehicles that have mutual occlusion.

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Keywords

  • Vehicle Detection
  • Vehicle Tracking
  • Adaptive Gaussian Mixture Model
  • Kalman Filter