Recovering Missing Contours for Occluded Object Detection

One difficult problem in practical applications is the corrupted or missing data frequently encountered in digital images. It introduces great challenges to the tasks such as object detection. This letter provides new methods for recovering missing object contours and detecting occluded objects. First, we propose an efficient contour reconstruction approach according to the Bayesian rule, utilizing global shape prior knowledge. Second, the contour reconstruction is applied to a robust detection framework for occluded objects. Based on the observed broken curves we iteratively recover object contours and propose object candidates. The experimental results demonstrate the high detection performance, localization accuracy and great advantages of our method for severe occlusion cases.