Improved vehicle speed estimation using Gaussian mixture model and hole filling algorithm

A. Nurhadiyatna; B. Hardjono; Ari Wibisono; I. Sina; Wisnu Jatmiko; M. Anwar Ma'Sum; Petrus Mursanto

doi:10.1109/ICACSIS.2013.6761617

Improved vehicle speed estimation using Gaussian mixture model and hole filling algorithm

A. Nurhadiyatna, B. Hardjono, Ari Wibisono, I. Sina, Wisnu Jatmiko, M. Anwar Ma'Sum, Petrus Mursanto

Research output: Contribution to conference › Paper › peer-review

25 Citations (Scopus)

Abstract

Vehicle speed estimation using Closed Circuit Television (CCTV) is one of the interesting issues in the field of computer vision. Various approaches are used to perform automation in vehicle speed estimation using CCTV. In this study, the use of Gaussian Mixture Model (GMM) for vehicle detection has been improved with the hole-filling method (HF). The speed estimation of the vehicles with various scenarios has been done, and gives the best estimation with the deviation of 7.63 Km/hr. GMM fusion with hole-filling algorithm combined with Pinhole models have shown the best results compared with results using other scenarios.

Original language	English
Pages	451-456
Number of pages	6
DOIs	https://doi.org/10.1109/ICACSIS.2013.6761617
Publication status	Published - 2013
Event	2013 5th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2013 - Bali, Indonesia Duration: 28 Sept 2013 → 29 Sept 2013

Conference

Conference	2013 5th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2013
Country/Territory	Indonesia
City	Bali
Period	28/09/13 → 29/09/13

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10.1109/ICACSIS.2013.6761617

Cite this

Nurhadiyatna, A., Hardjono, B., Wibisono, A., Sina, I., Jatmiko, W., Ma'Sum, M. A., & Mursanto, P. (2013). Improved vehicle speed estimation using Gaussian mixture model and hole filling algorithm. 451-456. Paper presented at 2013 5th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2013, Bali, Indonesia. https://doi.org/10.1109/ICACSIS.2013.6761617

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abstract = "Vehicle speed estimation using Closed Circuit Television (CCTV) is one of the interesting issues in the field of computer vision. Various approaches are used to perform automation in vehicle speed estimation using CCTV. In this study, the use of Gaussian Mixture Model (GMM) for vehicle detection has been improved with the hole-filling method (HF). The speed estimation of the vehicles with various scenarios has been done, and gives the best estimation with the deviation of 7.63 Km/hr. GMM fusion with hole-filling algorithm combined with Pinhole models have shown the best results compared with results using other scenarios.",

author = "A. Nurhadiyatna and B. Hardjono and Ari Wibisono and I. Sina and Wisnu Jatmiko and Ma'Sum, {M. Anwar} and Petrus Mursanto",

year = "2013",

doi = "10.1109/ICACSIS.2013.6761617",

language = "English",

pages = "451--456",

note = "2013 5th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2013 ; Conference date: 28-09-2013 Through 29-09-2013",

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Nurhadiyatna, A, Hardjono, B, Wibisono, A, Sina, I, Jatmiko, W , Ma'Sum, MA & Mursanto, P 2013, 'Improved vehicle speed estimation using Gaussian mixture model and hole filling algorithm', Paper presented at 2013 5th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2013, Bali, Indonesia, 28/09/13 - 29/09/13 pp. 451-456. https://doi.org/10.1109/ICACSIS.2013.6761617

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AU - Nurhadiyatna, A.

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AU - Wibisono, Ari

AU - Sina, I.

AU - Jatmiko, Wisnu

AU - Ma'Sum, M. Anwar

AU - Mursanto, Petrus

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AB - Vehicle speed estimation using Closed Circuit Television (CCTV) is one of the interesting issues in the field of computer vision. Various approaches are used to perform automation in vehicle speed estimation using CCTV. In this study, the use of Gaussian Mixture Model (GMM) for vehicle detection has been improved with the hole-filling method (HF). The speed estimation of the vehicles with various scenarios has been done, and gives the best estimation with the deviation of 7.63 Km/hr. GMM fusion with hole-filling algorithm combined with Pinhole models have shown the best results compared with results using other scenarios.

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T2 - 2013 5th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2013

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Improved vehicle speed estimation using Gaussian mixture model and hole filling algorithm

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