Investigation and performance analysis of OpenVX optimizations on computer vision applications

Abstract

The development of Advanced Driver Assistance Systems (ADAS), such as pedestrian detection, requires real-time update rates at high image resolution. Hopefully, heterogeneous architectures with high computing performance have been developed for this purpose. To benefit from this hardware performance, different programming languages and acceleration frameworks have been developed. OpenVX framework provides a graph-based execution model to program image processing algorithms on heterogeneous platforms. In this work, we investigate OpenVX optimizations for computer vision applications. We examine how this framework responds to different data access patterns. We test three important optimizations of OpenVX: kernels merge, data tiling and parallelization via OpenMP. The contribution and the impact of each optimization on different data access pattern are explained.