A genetically optimized graph-based people extraction method for embedded transportation systems real conditions

In this paper, we present a new method for people extraction in complex transport environments. Many background subtraction methods exist in the literature but don´t give satisfactory results on complex images acquired in moving trains that include several locks such as fast brightness changes, noise, shadow, scrolling background, etc. To tackle this problem, a new method for people extraction in images is proposed. It is based on an image superpixel segmentation coupled with graph cuts binary clustering, initialized by a state-of-the-art foreground detection method. The proposed strategy is composed of four blocks. A pre-processing block that uses filters and colorimetric invariants to limit the presence of artifacts in images. A foreground detection block that enables to locate moving people in images. A post-treatment block that removes shadow regions of no-interest. A people extraction block that segments the image into SLIC superpixels and performs a graph cut binary clustering to precisely extract people. Tests are realized on a real database of the BOSS European project and are evaluated with the standard F-measure criteria. Since many state-of-the-art methods can be considered in our three first blocks along with many associated parameters, a genetic algorithm is used to automatically find the best methods and parameters of the proposed approach.