Abstract
The embedded systems continue to display as solutions of smart surveillance systems. Background subtraction using Gaussian Mixture Model (GMM) is often portrayed as a common step for video processing. This work discusses the implementation of an embedded vision system on system-on-a-chip (SoC) device that integrates both a processor and an FPGA (Field Programmable Gate Array) architecture. The conventional Register Transfer Level (RTL) design, typically used for FPGA programming is slow, and the use of floating-point arithmetic is complex. However, the use of High-Level Synthesis (HLS) tools allows describing algorithms using high-level programming languages. Three background subtraction algorithms with floating-point arithmetic were developed using a hardware-software co-design methodology. The paper presents the details of the implementation on a ZedBoard Zynq Evaluation and Development Kit, considering requirements as hardware resources and power consumption used. Also, performance comparisons among a PC-based, ARM, FPGA and SOC-FPGA implementations are presented. The results showed that frame rates needed for real-time video processing were reached.
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Acknowledgements
This study was supported by the AE&CC research Group COL0053581, at the Sistemas de Control y Robótica Laboratory, attached to the Instituto Tecnológico Metropolitano. This work is part of the project “Improvement of visual perception in humanoid robots for objects recognition in natural environments using Deep Learning” with ID P17224, co-funded by the Instituto Tecnológico Metropolitano and Universidad de Antioquia. L. J. Morantes-Guzmán is under grants of “Convocatoria de Doctorados Nacionales 617 - COLCIENCIAS 2013”.
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Morantes-Guzmán, L.J., Alzate, C., Castano-Londono, L., Marquez-Viloria, D., Vargas-Bonilla, J.F. (2019). Performance Evaluation of SoC-FPGA Based Floating-Point Implementation of GMM for Real-Time Background Subtraction. In: Figueroa-García, J., Duarte-González, M., Jaramillo-Isaza, S., Orjuela-Cañon, A., Díaz-Gutierrez, Y. (eds) Applied Computer Sciences in Engineering. WEA 2019. Communications in Computer and Information Science, vol 1052. Springer, Cham. https://doi.org/10.1007/978-3-030-31019-6_11
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