Feature extraction of moving objects using background subtraction technique for robotic applications

Abstract

This paper aims to develop a background subtraction algorithm based on Gaussian Mixture Model (GMM) using Probability Density Function (PDF) to identify the location of moving objects over a belt conveyor for pick and place operations using an industrial robot. In the present work, a stationary webcam is placed above the conveyor system to capture images of the objects that are coming into the view field. The objects of interest are identified by subtracting the background image (reference frame) from the current image frame based on the probability density function of respective pixels over time. The subtracted image frame is processed to extract the attributes such as location, colour, and shape of the objects. The extracted information, in turn, helps the robot to pick the desired object of interest. The results indicated that the GMM based background subtraction is more precisely extracting the features of the object than the direct subtraction technique for robotic applications. The algorithm is developed using MATLAB software.