Vehicle Counting Method Based On Gaussian Mixture Models And Blob Analysis

Abstract

Combination of a Gaussian Mixture Models and Entropy to detect vehicles in motion from any sort of video sequences. The proposed method is composed of four major steps: background subtraction, improved GMM, Blob Analysis, Voting Scheme processing. An implementation of proposed technique has been performed using MATLAB R2017a. Vehicle detection process provides relevant information about traffic patterns, crash occurrences and traffic peak times in roadways. Detection of number of vehicles and traffic monitoring will be helpful by implementing motion and object detection using background subtraction, Monitoring traffic from video footages will provide a greater reality in terms of vehicle count. Gaussian Mixture Model is most advanced model in motion detection due to the reliability that it has shown in the background extraction and foreground segmentation process. Here we are going to find motion of object in video sequences under both static camera arrangement and dynamic background sequences by using background subtraction method and blob analysis approach and analyzing them