Vehicle detection and speed measurement using video image sequences

Pao Yue-kong Library Electronic Theses Database

Vehicle detection and speed measurement using video image sequences

 

Author: Yang, Zi
Title: Vehicle detection and speed measurement using video image sequences
Degree: M.Sc.
Year: 2015
Subject: Vehicle detectors.
Image processing -- Digital techniques.
Automobiles -- Speed -- Measurement.
Hong Kong Polytechnic University -- Dissertations
Department: Faculty of Construction and Environment
Pages: x, 93 pages : color illustrations
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b2831597
URI: http://theses.lib.polyu.edu.hk/handle/200/8348
Abstract: As an important part of ITS, vehicle detection has received considerable attention in recent years. The dissertation presents an approach for detecting vehicles and speed measurement from video image sequences. CCTV cameras are utilized as sensors for data acquisition. Vehicles detection is divided into two parts image preprocessing and object extraction. Image preprocessing aims to remove background interference by a region mask, and reduce unwanted noise through the Gaussian smooth filter. In the object extraction part, vehicles are detected using color and edge. Prominent vehicles are detected using selected RGB ranges and the Sobel edge detector. By finding horizontal and vertical range of a detected box, vehicle(s) can be finally detected from video image sequences. After finding possible vehicle candidates, vehicle speed is computed using pre-computed distance and fixed time interval. Corresponding coordinates are found in video image and Google Earth to establish a proportional relationship between image and reality, known as pre-computed distance. Computed vehicle speed is compared with speed display unit recorded by a mobile phone as evaluation. In the experiments, proposed methods are evaluated on two scenarios,sparse traffic condition and congested traffic condition. Target vehicles are green minibuses in both cases.Experimental results show vehicle(s) can be stably and efficiently detected from video image sequences. The proposed methods also work fairly to measure vehicle speed.

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