In recent years, we are witnessing a huge growth in the number of surveillance cameras and applications based on live video analysis. Analyzing this huge amount of video feeds requires enormous computation and bandwidth. To ease the burden of live video analytics, edge computing has been proposed to bring resources to the proximity of data. However, the number of cameras is ever-growing and the associated computing resources on edge will again fall in short. To fundamentally solve the resource scarcity problem and make edge-based live video analytics scalable, we present an FPGA-based smart camera design that enables efficient streaming processing to meet the stringent low-power, energy-efficient, low-latency requirements of edge vision applications. By leveraging FPGA\u2019s intrinsic properties of architecture efficiency and exploiting its hardware support for parallelism, we demonstrate a 49x speedup over ARM CPU and 6.4x more energy-efficiency than GPU, verified using a background subtraction algorithm.<\/p>\n","protected":false},"excerpt":{"rendered":"
In recent years, we are witnessing a huge growth in the number of surveillance cameras and applications based on live video analysis. Analyzing this huge amount of video feeds requires enormous computation and bandwidth. To ease the burden of live video analytics, edge computing has been proposed to bring resources to the proximity of data. 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