Parallelized Background Substitution System on a Multi-core Embedded Platform

We present an automatic human background substitution system based on a Random Walk (RW) algorithm on a multi-core processing architecture. Firstly, a fast algorithm is proposed to solve the large linear system in RW based on adapting the Gauss-Seidel method. Two tables, TYPE and INDEX, are introduced to fast locate the required data for the close-form solution. Then, face detection along with a human shape prior model are exploited to decide the approximated human body and background area. Pixels inside these areas are used as seed points in RW algorithm for automatic segmentation. The proposed method is designed to be highly parallelizable and suitable for running on a multi-core architecture. We demonstrate the parallelization strategies for the proposed fast RW algorithm and face detection on heterogeneous multi-core embedded platform to make the most use of the system architecture. Compared to the single processor implementation, the experimental results show significant speedup ratio of the parallelized human background substitution system on a multi-core embedded platform, which consists of an ARM processor and two DSP cores.