On implementing motion-based Region of Interest detection on multi-core CELL

Region of Interest (ROI) detection is a well-studied problem in computer vision for applications such as video surveillance and vision-based robotics. ROI detection may be done using background subtraction schemes with change detection and background estimation. When the camera is not static, these schemes will be ineffective and hence there is a need for global motion estimation (GME) to compensate the camera motion. Robust GME algorithms often require high computation power, rendering them unsuitable for real-time, embedded vision applications. In this article, we use a multi-core processor platform - CELL, to meet the computational requirements of the ROI detection system and to explore the feasibility of potential usage of such heterogeneous processor architecture for vision applications. In particular, we analyze the algorithmic components of a typical GME-based ROI detection system and show how to make efficient use of the parallel and vector computation capabilities in the CELL cores for maximizing the gain on speed performance. We have also ported our system on a Sony PS3 system and promising results have been achieved. Based on the study, various design aspects and implementation challenges are discussed which are believed to be useful for future work in porting vision algorithms on multi-core architectures for real-time embedded applications.