Detecting Half-Occlusion with a Fast Region-Based Fusion Procedure

This paper presents a novel region-based approach for detecting occlusion between two consecutive frames. Based on a generalization of Marr and Poggio’s uniqueness assumption, the explicit goal of our method is to reduce the number of false positives while optimizing the hit rate. To do so, our method relies on a fusion procedure that blends together two segmentation maps: one pre-estimated occlusion binary map and one color segmentation map. While the occlusion map is obtained after a simple thresholding procedure, the color segmentation map is obtained with an unsupervised Markovian approach. Assuming that the color segmentation regions exhibit more precise edges, the occlusion areas are iteratively modified to fit the colorregion shapes. Since our method has been entirely implemented on a parallel architecture (a Graphics Processor Unit), its processing times are remarkably low. Our method is compared with other occlusion approaches both quantitatively and qualitatively on scenes that represent different challenges.