2019/04/30 by Eran Goldman, Roei Herzig, Aviv Eisenschtat +4
Computer Science · #cs.CV
published as IEEE Conference on Computer Vision and Pattern Recognition, 2019 · CVPR 2019
arxiv created 2019/04/30 · arxiv updated 2019/11/19
Man-made scenes can be densely packed, containing numerous objects, often identical, positioned in close proximity. We show that precise object detection in such scenes remains a challenging frontier even for state-of-the-art object detectors. We propose a novel, deep-learning based method for precise object detection, designed for such challenging settings. Our contributions include: (1) A layer for estimating the Jaccard index as a detection quality score; (2) a novel EM merging unit, which uses our quality scores to resolve detection overlap ambiguities; finally, (3) an extensive, annotated data set, SKU-110K, representing packed retail environments, released for training and testing under such extreme settings. Detection tests on SKU-110K and counting tests on the CARPK and PUCPR+ show our method to outperform existing state-of-the-art with substantial margins. The code and data will be made available on \urlwww.github.com/eg4000/SKU110KCVPR19.