Sheeraz Memon

Work place: Department of Computer System Engineering, Mehran University of Engineering & Technology, Jamshoro, Pakistan.



Research Interests: Speech Synthesis, Speech Recognition, Image Processing, Image and Sound Processing, Image Manipulation, Image Compression, Pattern Recognition


Mr. Memon left for securing the doctoral degree from RMIT University Australia. He is skilled in Digital Signal Processing (DSP), Voice/Speech Feature Extraction, Machine learning/Pattern Recognition and Digital Image ProceSheeraz Memon – He is working with the Department of Computer Systems Engineering, Mehran UET, Sindh, Pakistan, since Sep, 2004. He completed his Bachelor of Engineering in computer systems in Feb, 2004 and Master of Engineering in Communication Systems and Networks in Feb, 2007, from Mehran, UET Jamshoro. The same year he was awarded the PhD, Scholarship under faculty development program, and ssing.

Author Articles
A Video based Vehicle Detection, Counting and Classification System

By Sheeraz Memon Sania Bhatti Liaquat A. Thebo Mir Muhammad B. Talpur Mohsin A. Memon

DOI:, Pub. Date: 8 Sep. 2018

Traffic Analysis has been a problem that city planners have dealt with for years. Smarter ways are being developed to analyze traffic and streamline the process. Analysis of traffic may account for the number of vehicles in an area per some arbitrary time period and the class of vehicles. People have designed such mechanism for decades now but most of them involve use of sensors to detect the vehicles i.e. a couple of proximity sensors to calculate the direction of the moving vehicle and to keep the vehicle count. Even though over the time these systems have matured and are highly effective, they are not very budget friendly. The problem is such systems require maintenance and periodic calibration. Therefore, this study has purposed a vision based vehicle counting and classification system. The system involves capturing of frames from the video to perform background subtraction in order detect and count the vehicles using Gaussian Mixture Model (GMM) background subtraction then it classifies the vehicles by comparing the contour areas to the assumed values. The substantial contribution of the work is the comparison of two classification methods. Classification has been implemented using Contour Comparison (CC) as well as Bag of Features (BoF) and Support Vector Machine (SVM) method. 

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