An Efficient Architecture Solution for Low-Power Real-Time Background Subtraction

Embedded vision is a rapidly growing market with a host of challenging algorithms. Among vision algorithms, Mixture of Gaussian (MoG) background subtraction is a frequently used kernel involving massive computation and communication. Tremendous challenges need to be reolved to provide MoG's high computation and communication demands with minimal power consumption allowing its embedded deployment. This paper proposes a customized architecture for power-efficient realization of MoG background subtraction operating at Full-HD resolution. Our design process benefits from system-level design principles. An SLDL-captured specification (result of high-level explorations) serves as a specification for architecture realization and hand-crafted RTL design. To optimize the architecture, this paper employs a set of optimization techniques including parallelism extraction, algorithm tuning, operation width sizing and deep pipelining. The final MoG implementation consists of 77 pipeline stages operating at 148.5 MHz implemented on a Zynq-7000 SoC. Furthermore, our background subtraction solution is flexible allowing end users to adjust algorithm parameters according to scene complexity. Our results demonstrate a very high efficiency for both indoor and outdoor scenes with 145 mW on-chip power consumption and more than 600x speedup over software execution on ARM Cortex A9 core.