Man-made scenes are often 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.
|Title of host publication||Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019|
|Publisher||IEEE Computer Society|
|Number of pages||10|
|State||Published - Jun 2019|
|Event||32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019 - Long Beach, United States|
Duration: 16 Jun 2019 → 20 Jun 2019
|Name||Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition|
|Conference||32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019|
|Period||16/06/19 → 20/06/19|
Bibliographical notePublisher Copyright:
© 2019 IEEE.
- Recognition: Detection