Rethinking Object Detection in Retail Stores
- URL: http://arxiv.org/abs/2003.08230v3
- Date: Sat, 5 Dec 2020 05:14:15 GMT
- Title: Rethinking Object Detection in Retail Stores
- Authors: Yuanqiang Cai, Longyin Wen, Libo Zhang, Dawei Du, Weiqiang Wang
- Abstract summary: We propose a new task, simultaneously object localization and counting, abbreviated as Locount.
Locount requires algorithms to localize groups of objects of interest with the number of instances.
We collect a large-scale object localization and counting dataset with rich annotations in retail stores.
- Score: 55.359582952686175
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The convention standard for object detection uses a bounding box to represent
each individual object instance. However, it is not practical in the
industry-relevant applications in the context of warehouses due to severe
occlusions among groups of instances of the same categories. In this paper, we
propose a new task, ie, simultaneously object localization and counting,
abbreviated as Locount, which requires algorithms to localize groups of objects
of interest with the number of instances. However, there does not exist a
dataset or benchmark designed for such a task. To this end, we collect a
large-scale object localization and counting dataset with rich annotations in
retail stores, which consists of 50,394 images with more than 1.9 million
object instances in 140 categories. Together with this dataset, we provide a
new evaluation protocol and divide the training and testing subsets to fairly
evaluate the performance of algorithms for Locount, developing a new benchmark
for the Locount task. Moreover, we present a cascaded localization and counting
network as a strong baseline, which gradually classifies and regresses the
bounding boxes of objects with the predicted numbers of instances enclosed in
the bounding boxes, trained in an end-to-end manner. Extensive experiments are
conducted on the proposed dataset to demonstrate its significance and the
analysis discussions on failure cases are provided to indicate future
directions. Dataset is available at
https://isrc.iscas.ac.cn/gitlab/research/locount-dataset.
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