Product counting using images with application to robot-based retail stock assessment
|
Accepted Version
(PDF 2MB)
68086240. |
Description
In this paper, we propose a novel method for obtaining product count directly from images recorded using a monocular camera mounted on a mobile robot. This has application in robot-based retail stock assessment problem where a mobile robot is used for monitoring the stock levels on the shelves of a retail store. The products are recognized by carrying out a nearest-neighbor search in the template feature space using a k-d tree. Unlike current approaches which only provide approximate stock level, we propose a method which can compute the exact number of discrete products visible in a given image. The product count is obtained by fitting bounding box around each product and removing them sequentially from the image. A second stage of grid-based search is carried out in the neighborhood of each detected product to detect new products which were missed out in the previous step. This detection is based on a confidence measure that includes various information such as histogram matching and spatial location. The efficacy of the proposed approach is demonstrated through experiments on different datasets obtained using robot camera as well as mobile phone camera. These results show that the robot-based retail stock assessment may become a viable alternative to the currently prevailing manual mode of carrying out these surveys.
Impact and interest:
Citation counts are sourced monthly from Scopus and Web of Science® citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.
Full-text downloads:
Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.
ID Code: | 204527 | ||
---|---|---|---|
Item Type: | Chapter in Book, Report or Conference volume (Conference contribution) | ||
Series Name: | IEEE Conference on Technologies for Practical Robot Applications, TePRA | ||
ORCID iD: |
|
||
Measurements or Duration: | 6 pages | ||
Keywords: | object recognition, OOS, product counting, Retail Robotics, service robotics, stock assessment | ||
DOI: | 10.1109/TePRA.2015.7219676 | ||
ISBN: | 9781479987573 | ||
Pure ID: | 68086240 | ||
Copyright Owner: | IEEE 2015 | ||
Copyright Statement: | This work is covered by copyright. Unless the document is being made available under a Creative Commons Licence, you must assume that re-use is limited to personal use and that permission from the copyright owner must be obtained for all other uses. If the document is available under a Creative Commons License (or other specified license) then refer to the Licence for details of permitted re-use. It is a condition of access that users recognise and abide by the legal requirements associated with these rights. If you believe that this work infringes copyright please provide details by email to qut.copyright@qut.edu.au | ||
Deposited On: | 16 Sep 2020 23:40 | ||
Last Modified: | 26 May 2024 18:01 |
Export: EndNote | Dublin Core | BibTeX
Repository Staff Only: item control page