FPGA-based object-extraction based on multimodal Σ-Δ background estimation

In this paper, we propose a robust and accurate algorithm based on a multimodal Sigma-Delta background estimation to extract the moving objects in image sequence of size 768 x 576 pixels taken from a static camera. Sigma-Delta estimation is used to compute two orders of temporal statistics for each pixel of the sequence providing a pixel-level decision framework. A serious limitation of this approach lies in the adaptation capability to certain complex scenes. In this paper, we avoid this limitation by modeling each pixel as mixture of three distributions to deal with complex scenes. We show that the enhanced performance is achieved by using the proposed algorithm. This paper describes also an FPGA-based implementation of the proposed algorithm at a very high frame rate that reaches to 1198 frames per second in a single low cost FPGA chip, which is adequate for most real-time vision applications.