Abstract Detecting pedestrians in cluttered scenes is a challenging problem in computer vision. The dif?culty is added when several pedestrians overlap in images and occlude each other. We observe, however, that the occlusion/visibility statuses of overlapping pedestrians provide useful mutual relationship for visibility estimation the vi ibility estimation of one pedestrian facilitates the visibility estimation of another. In this paper, we propose a mutual visibility deep model that jointly estimates the visibil ity statuses of overlapping pedestrians. The visibility relationship among pedestrians is learned from the deep model for recognizing co-existing pedestrians. Experimental results show that the mutual visibility deep model effectively improves the pedestrian detection results. Compared with existing image-based pedestrian detection approaches, our approach has the lowest average miss rate on the CaltechTrain dataset, the Caltech-Test dataset and the ETH dataset. Including mutual visibility leads to 4% ? 8% improvements on multiple benchmark datasets.