A comparative study of algorithms for vehicle detection with elevated video images

Pao Yue-kong Library Electronic Theses Database

A comparative study of algorithms for vehicle detection with elevated video images


Author: Liu, Yachen
Title: A comparative study of algorithms for vehicle detection with elevated video images
Degree: M.Sc.
Year: 2012
Subject: Traffic flow -- Mathematical models.
Image analysis -- Mathematical models.
Hong Kong Polytechnic University -- Dissertations
Department: Dept. of Land Surveying and Geo-Informatics
Pages: viii, 53, xiv leaves : ill. (some col.), 1 col. map ; 30 cm.
Language: English
OneSearch: https://www.lib.polyu.edu.hk/bib/b2475290
URI: http://theses.lib.polyu.edu.hk/handle/200/6522
Abstract: With the population growth and increasing of vehicles, traffic problem especially congestion problems become very serious. Vehicle detection from video sequence is the basic and important part of traffic surveillance and tracking. Detected vehicles can be further processed for vehicle tracking, calculate average vehicle speed, vehicle flowrate and occupancy. It is essential to detect moving vehicles stably and efficiently especially in complicated background environments. Researchers have proposed many vehicle detection methods. In this thesis, three vehicle detection algorithms including two simple methods frame differencing and approximated median filter and one complicated method Mixture of Gaussians were performed. An experiment including three test sequences with different traffic conditions and different illumination was designed to test performances of three algorithms. Based on the results, qualitative comparison with the performance of detecting slow moving vehicles, performance of vehicle completeness, remove noise and remove cast shadows were performed. Quantitative comparative analysis with recall and precision value was given based on experiment results in the first and the third test sequence. Results show that frame differencing algorithm is the fastest and simplest method among three algorithms. The speed of background updating is fast to eliminate noise such as moving leaves and traffic lanes. Approximated median filter algorithm is easy to implement too and holds a high recall and precision value. Mixture of Gaussians is the most complicated algorithm among three algorithms, but performance of Mixture of Gaussians method in this experiment is not so good because of noise and "holes" on vehicles.

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