Abstract: Background and Objectives: The study was focused to make use of committed hardware structural design for moving object segmentation. The competent architecture by the improved performance algorithm to produce accurate results was proposed in this study. The objective of this study demonstrated: (1) Accurate motion object segmentation algorithm intended for video supervision system. (2) Implementation and study of its computational complexity of proposed algorithm architecture on Hardware Accelerators (Field Programmable Gate Arrays and Application Specific Integrated Circuits). Methodology: To accumulate the objectives the simulation was conducted to evaluate and generate the accurate measures using the Background Modeling along with Biased Illumination Field Fuzzy C-Means (BM-BIFCM) algorithm. For the examination of the mentioned algorithm performance, the standard video was considered and corresponding values of proposed algorithm was derived using Matlab tool. The architecture implemented on Xilinx Vivado Field programmable gate arrays devices via Very High Speed Integrated Circuit Hardware Description Language or Verilog code in Integrated Software Environment tools fitting and same in Application Specific Integrated Circuits using Cadence tools. Results: The effect of the algorithm was demonstrated as considerable proof to boost the correctness of segmentation procedure using metrics and execution on hardware outcome illustrated the complexity of architecture decreased in both Field Programmable Gate Array (FPGA) and Application Specific Integrated Circuits (ASIC). Conclusion: The response of the suggested method produced accurate results, so that it may be relevant in real time applications efficiently. The implementation obstacles reduced in the direction of chip area, power and delay on hardware architecture, so that cost of the chip design diminished by using the presented algorithm.
Siva Nagi Reddy Kalli and Bhanu Murthy Bhaskara, 2017. Implementation of Moving Object Segmentation using Background Modeling with Biased Illumination Field Fuzzy C-Means on Hardware Accelerators. Asian Journal of Scientific Research, 10: 139-149.