Abstract：RoIs extraction is a challenging task due to scene complexity,less image texture and unstable image quality.Paying more attention to local pedestrian details and neighborhood relations of pixels,threshold segmentation is prone to give rise to mis-segmentation such as adhesion,omission,breakage and uncontrollable RoIs amount.Imitating human’s eyes,we focus on an image saliency region,size and location and propose a new RoIs extraction method using probability map.Design convex-concave curves to map an image pixel gray for enhancing the image contrast;get a saliency map based on image signature manner;fuse intensity and saliency probability images and then extract the image foreground;design an algorithm to search the probability map region limited by a road horizon and generate RoIs.Experimental results show that our method can improve RoIs locating accuracy,control RoIs amount and reduce non-pedestrian RoIs when comparing with threshold segmentation method.Our recall increases no less than 9% when same RoIs amount are extracted.
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2020, Vol. 48 
