Segmentation of Far-infrared pedestrians for advanced driver-assistance systems

Robust and efficient Far-infrared pedestrian segmentation under outdoor environment is challenging for advanced driver-assistance systems (ADAS). In order to address the problems that the existing methods are easy to be interfered by various background targets, various pedestrian scales and images noise, a novel pedestrian segmentation method in far-infrared image for ADAS is proposed to address the above three problems. Firstly, with the purpose of reducing the interference of background targets, we design a road horizontal plane estimation algorithm to locate the area of interest (AOI) and a pixel-intensity vertical projection is utilized within the AOI. Secondly, in order to reduce the interference of various pedestrian scales, the width of each estimated vertical image stripe (denotes the width of a pedestrian) is regarded as a key parameter to guide the dual-threshold segmentation algorithm. Thirdly, we use proper morphological operations to deal with the image noise. Experiments conducted on extensive urban image sequences indicate that, compared with two state-of-the-art algorithms, the method proposed in this research is more reliable and feasible for practical applications.