FPGA Implementation of GMM Algorithm for Background Subtractions in Video Sequences

  • S. ArivazhaganEmail author
  • K. Kiruthika
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 460)


Moving object detection is an important feature for video surveillance based applications. Many background subtraction methods are available for object detection. Gaussian mixture modeling (GMM) is one of the best methods used for background subtraction which is the first and foremost step for video processing. The main objective is to implement the Gaussian mixture modeling (GMM) algorithm in Field-Programmable Gate Array (FPGA). In this proposed GMM algorithm, three Gaussian parameters are taken and the three parameters with learning rate over the neighborhood parameters were updated. From the updated parameters, the background pixels are classified. The background subtraction has been performed for consecutive frames by the updated parameters. The hardware architecture for Gaussian mixture modeling has been designed. The algorithm has been performed in offline from the collected data set. It can able to process up to frame size of 240 × 240.


Background subtraction Moving object detection Gaussian mixture modeling (GMM) Hardware architecture Field programmable gate array (FPGA) 



The authors wish to express humble gratitude to the Management and Principal of Mepco Schlenk Engineering College, for the support in carrying out this research work.


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Copyright information

© Springer Science+Business Media Singapore 2017

Authors and Affiliations

  1. 1.Department of ECEMepco Schlenk Engineering CollegeSivakasiIndia

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