Fast image foreground object segmentation based on block texture for embedded system implementation

Abstract

In this paper, a novel block-based foreground object detection method based on block texture is presented. It can significantly reduce the memory usage when constructing the background model in dynamic scenes. The proposed background model and detection algorithm are suitable for implementing on embedded system platforms with resource limitations. The experimental results of processing benchmark videos show that our method has outcomes that are very close to ground truth segmentation. In addition, the proposed method requires approximately 23.97% less memory than the latest algorithms. Finally, the proposed approach is implemented on an embedded system platform. The processing speed can achieve a real-time rate of at least 20 fps, which is an improvement of 17.64% as compared to the latest algorithms.