Single Core SIMD Parallelization of GMM Background Subtraction Algorithm for Vehicles Detection

Mabrouk, Lhoussein; Houzet, Dominique; Huet, Sylvain; Belkouch, Saïd; Hamzaoui, Abdelkrim; Zennayi, Yahya

doi:10.1109/cist.2018.8596385

Cited by 5 publications

(1 citation statement)

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“…GMM is a robust method for dynamic backgrounds [11]. The advantage of the GMM algorithm can show multi-mode changes in pixel values, account lighting changes, repetitive space movements and scene changes in real time [12], [13]. The Automatic Number Plate Recognition (ANPR) method is a method proposed to Identification Vehicle Number.…”

Section: Proposed Methodsmentioning

confidence: 99%

Tracking of Vehicle Tax Violations Using Vehicle Type and Plate Number Identification

Jaya¹,

Zainuddin²,

Syarif³

2019

Proceedings of the 1st International Conference on Science and Technology, ICOST 2019, 2-3 May, Makassar, Indonesia

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This paper describes tracking violations of vehicle tax using the identification of type and number plates on vehicles. The case study of this research is in Sulawesi Selatan (Indonesia). The Gaussian Mixture Model (GMM) algorithm is used to detect vehicle types and the Automatic Number Plate Recognition (ANPR) algorithm for detecting vehicle license numbers. The proposed system uses a digital camera with a camera height of 250 cm and a camera tilt angle of 55 degrees. The proposed system design is the stages of preprocessing, feature extraction, feature selection, model selection, and database. For the introduction of types of vehicles are classified based on predetermined ROI while the introduction of vehicle numbers is trained using k-Nearest Neighbor (KNN). The results of the proposed system accuracy are 91% for vehicle type detection and 70% for vehicle license number detection.

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Section: Proposed Methodsmentioning

confidence: 99%