Algorithmic and software techniques for embedded vision on programmable processors

In the last few years, programmable architectures centered around high-end DSP processors have emerged as the platform of choice for high-volume embedded vision applications, such as automotive safety and video surveillance. Their programmability inherently addresses the problems presented by the sheer diversity of vision algorithms. This paper provides an overview of high-impact algorithmic and software techniques for embedded vision applications implemented on programmable architectures and discusses several system-level issues. We provide a general discussion and practical examples for the following categories of algorithmic techniques: fast algorithms, reduced dimensionality and mathematical shortcuts. Additionally, we discuss the importance of software techniques such as the use of fixed-point arithmetic, reduced data transfers and cache-friendly programming. In our experience, each of these techniques is a key enabler for real-time embedded vision systems.