Reliable object detection and segmentation using inpainting

This paper presents a novel object detection and segmentation method utilizing an inpainting algorithm. Inpainting is a concept of recovering missing image regions based on their surroundings, which were originally used for restoration of damaged paintings. In this paper, we newly utilize inpainting to judge whether an object candidate region includes the foreground object or not. The key idea is that if we erase a certain region from an image, the inpainting algorithm is expected to recover the erased image only when it belongs a background area (i.e. only when there is no object in it). By measuring the similarity between the inpainted region and the original image region, our approach filters out false detections while maintaining true object detections. Furthermore, we take advantage of the inpainting for object segmentation, since our approach is designed to explicitly distinguish foreground areas from its background. Experimental results confirm that our approach applied to baseline detectors enables better recognition of objects, obtaining higher accuracies. We illustrate how our inpainting-based detection/segmentation approach benefits the object detection using two different pedestrian datasets.