Feature extraction of moving objects using background subtraction technique for robotic applications
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Feature extraction of moving objects using background subtraction technique for robotic applications Pramod Kumar Thotapalli1 · CH. R. Vikram Kumar2 · B. Chandra Mohana Reddy3 Received: 27 November 2019 / Accepted: 3 August 2020 © Springer Nature Singapore Pte Ltd. 2020
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. Keywords Gaussian mixture model · Probability density function · Background subtraction · Object of interest
1 Introduction Industrial robots play a vital role and are an integral part of automating processes such as material handling, assembly, welding, and many operations. Controlling the robot under dynamic conditions is a challenging task. In dynamic conditions, vision assisted artificial intelligence helps the robot to perceive reliable information to elevate the quality and productivity of the processes. The vision-enabled system enhances the degree of autonomy of the robot. A sophisticated artificial intelligence vision system is needed to * CH. R. Vikram Kumar [email protected] Pramod Kumar Thotapalli [email protected] B. Chandra Mohana Reddy [email protected] 1
JNTUA Research Centre, NBKRIST, Vidyanagar, Andhra Pradesh, India
2
Mechanical Engineering, NBKR Institute of Science and Technology, SPSR Nellore, Vidyanagar, Andhra Pradesh 524413, India
3
Department of Mechanical, JNTUACE, Anantapuramu 515002, India
evaluate the perceived evidence in copious images at every stage of production processes by using image-processing techniques. Artificial intelligence systems use vision algorithms built by certain mathematical functions to extract the information in each stage of image processing, like segmentation, thresholding, and clustering (Digital Image Processing 2009). Comprehensive statistical information of respective pixels data is useful to extract the features such as location, shape, colour, and size of the object of interest in the image utilizing those functions. Background subtraction is most reliable technique to extract the multiple features of the mov
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