Abstract. Object occlusion boundary detection is a fundamental and crucial research problem in computer vision. Solving this problem is challenging as we encounter extreme boundary/non-boundary class imbalance during the training of an object occlusion boundary detector. In this paper, we propose to address this class imbalance by up-weighting the loss contribution of false negative and false positive examples with our novel Attention Loss function. We also propose a unified end-to-end multi-task deep object occlusion boundary detection network (DOOBNet) by sharing convolutional features to simultaneously predict object boundary and occlusion orientation. DOOBNet adopts an encoderdecoder structure with skip connection in order to automatically learn multi-scale and multi-level features. We significantly surpass the stateof-the-art on the PIOD dataset (ODS F-score of .702) and the BSDS ownership dataset (ODS F-score of .555), as well as improving the detecting speed to as 0.037s per image on the PIOD dataset.
DOOBNet REF adopts an encoder-decoder structure with skip connection to recover occlusion boundaries and occlusion relationship.
DOOBNet: Deep Object Occlusion Boundary Detection from an Image
ArXiv
ArXiv
Computer Science