Abstract
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.
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Acknowledgements
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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Arivazhagan, S., Kiruthika, K. (2017). FPGA Implementation of GMM Algorithm for Background Subtractions in Video Sequences. In: Raman, B., Kumar, S., Roy, P., Sen, D. (eds) Proceedings of International Conference on Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol 460. Springer, Singapore. https://doi.org/10.1007/978-981-10-2107-7_33
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DOI: https://doi.org/10.1007/978-981-10-2107-7_33
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