Mir Muhammad B. Talpur

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

E-mail: baqir.talpur@gmail.com


Research Interests: Image Processing, Image Manipulation, Image Compression, Computer Vision, Computational Learning Theory, Computer systems and computational processes


Mir Muhammad Baqir Talpur – He completed his degree of B.E in computer systems engineering from Mehran University of Engineering and Technology, Jamshoro, Sindh, Pakistan in 2016. He has worked with different student organizations as a volutneer during his undergraduate studies. His research interests include Computer Vision, Image Processing, Machine Learning and Data Science.

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: https://doi.org/10.5815/ijigsp.2018.09.05, 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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