Sort the objects in the dense scenes .

Overview :


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 \url{www.github.com/eg4000/SKU110K_CVPR19}.





A typical image in our SKU-110K, showing densely packed objects. (a) Detection results for the state-of-the-art RetinaNet[2], showing incorrect and overlapping detections, especially for the dark objects at the bottom which are harder to separate. (b) Our results showing far fewer misdetections and better fitting bounding boxes. (c) Zoomed-in views for RetinaNet[2] and (d) our method.


Soft-IoU layer, added to an object detector to estimate the Jaccard index between the detected box and the (unknown) ground truth box.

EM-Merger unit, which converts detections and Soft-IoU scores into a MoG (Mixture of Gaussians), and resolves overlapping detections in packed scenes.A new dataset and benchmark, the store keeping unit, 110k categories (SKU-110K), for item detection in store shelf images from around the world.


I propose learning the Jaccard index with a soft Intersection over Union (Soft-IoU) network layer. This measure provides valuable information on the quality of detection boxes. Those detections can be represented as a Mixture of Gaussians (MoG), reflecting their locations and their Soft-IoU scores. Then, an Expectation-Maximization (EM) based method is then used to cluster these Gaussians into groups, resolving detection overlap conflicts.





System diagram: (a) Input image. (b) A base network, with bounding box (BB) and objectness (Obj.) heads, along with our novel Soft-IoU layer. (c) Our EM-Merger converts Soft-IoU to Gaussian heat-map representing (d) objects captured by multiple, overlapping bounding boxes. (e) It then analyzes these box clusters, producing a single detection per object



Dependencies

TBD.


Usage

TBD.


References

[1] Eran Goldman*, Roei Herzig*, Aviv Eisenschtat*, Jacob Goldberger, Tal Hassner, Precise Detection in Densely Packed Scenes, 2019.

[2] Tsung-Yi Lin, Priyal Goyal, Ross Girshick, Kaiming He, Piotr Dollar, Focal loss for dense object detection, 2018.


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