Illegal Parking Detection Using Gaussian Mixture Model and Kalman Filter

被引：10

作者：

Alkhawaji, Rami ^{[1
]}

Sedky, Mohamed ^{[1
]}

Soliman, Abdel-Hamid ^{[1
]}

机构：

[1] Staffordshire Univ, Fac Comp Engn & Sci, Stafford, England

来源：

2017 IEEE/ACS 14TH INTERNATIONAL CONFERENCE ON COMPUTER SYSTEMS AND APPLICATIONS (AICCSA) | 2017年

关键词：

tracking; detection; kalman; illegal parking; VEHICLE DETECTION; TRACKING; ROBUST; ROAD;

D O I：

10.1109/AICCSA.2017.212

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Automatic analysis of videos for traffic monitoring has been an area of significant research in the recent past. In this paper, we proposed a system to detect and track illegal vehicle parking using Gaussian Mixture Model and Kalman Filter. i-LIDS dataset is used to test and evaluate the algorithm by comparing the results with the ground truth provided, we have tested the system using 4 full videos from i-LIDS to detect parked vehicle whiten specific area. Region of interest has been used to detect Vehicle parks in a no parking zone over sixty seconds and remains stationary. Within the scope of this work, we highlighted the components of an automated traffic surveillance system, including background modeling, foreground extraction, Kalman filter and Gaussian mixture model.

引用

页码：840 / 847

页数：8

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