| 1. Identity statement | ||||
| Reference Type | Conference Paper (Conference Proceedings) | |||
| Site | sibgrapi.sid.inpe.br | |||
| Holder Code | ibi 8JMKD3MGPEW34M/46T9EHH | |||
| Identifier | 8JMKD3MGPEW34M/3U2AEMS | |||
| Repository | sid.inpe.br/sibgrapi/2019/09.07.22.07 | |||
| Last Update | 2019:09.07.22.07.19 (UTC) administrator | |||
| Metadata Repository | sid.inpe.br/sibgrapi/2019/09.07.22.07.20 | |||
| Metadata Last Update | 2022:06.14.00.09.29 (UTC) administrator | |||
| DOI | 10.1109/SIBGRAPI.2019.00029 | |||
| Citation Key | JrSilvVieiOliv:2019:DeLeAp | |||
| Title | RetailNet: A deep learning approach for people counting and hot spots detection in retail stores  | |||
| Format | On-line | |||
| Year | 2019 | |||
| Access Date | 2024, Apr. 28 | |||
| Number of Files | 1 | |||
| Size | 2451 KiB | |||
| 2. Context | ||||
| Author | 1 Jr. , Valério Nogueira 2 Silva, José Augusto 3 Vieira, Thales 4 Oliveira, Krerley | |||
| Affiliation | 1 Federal University of Alagoas (UFAL) 2 Federal University of Alagoas (UFAL) 3 Federal University of Alagoas (UFAL) 4 Federal University of Alagoas (UFAL) | |||
| Editor | Oliveira, Luciano Rebouças de Sarder, Pinaki Lage, Marcos Sadlo, Filip | |||
| e-Mail Address | thalesv@gmail.com | |||
| Conference Name | Conference on Graphics, Patterns and Images, 32 (SIBGRAPI) | |||
| Conference Location | Rio de Janeiro, RJ, Brazil | |||
| Date | 28-31 Oct. 2019 | |||
| Publisher | IEEE Computer Society | |||
| Publisher City | Los Alamitos | |||
| Book Title | Proceedings | |||
| Tertiary Type | Full Paper | |||
| History (UTC) | 2019-09-07 22:07:20 :: thalesv@gmail.com -> administrator :: 2022-06-14 00:09:29 :: administrator -> thalesv@gmail.com :: 2019 | |||
| 3. Content and structure | ||||
| Is the master or a copy? | is the master | |||
| Content Stage | completed | |||
| Transferable | 1 | |||
| Version Type | finaldraft | |||
| Keywords | deep learning computer vision people counting crowd estimation | |||
| Abstract | Customer behavior analysis is an essential issue for retailers, allowing for optimized store performance, enhanced customer experience, reduced operational costs, and consequently higher profitability. Nevertheless, not much attention has been given to computer vision approaches to automatically extract relevant information from images that could be of great value to retailers. In this paper, we present a low-cost deep learning approach to estimate the number of people in retail stores in real-time and to detect and visualize hot spots. For this purpose, only an inexpensive RGB camera, such as a surveillance camera, is required. To solve the people counting problem, we employ a supervised learning approach based on a Convolutional Neural Network (CNN) regression model. We also present a four channel image representation named RGBP image, composed of the conventional RGB image and an extra binary image P representing whether there is a visible person in each pixel of the image. To extract the latter information, we developed a foreground/background detection method that considers the peculiarities of people behavior in retail stores. The P image is also exploited to detect the hot spots of the store, which can later be visually analyzed. Several experiments were conducted to validate, evaluate and compare our approach using a dataset comprised of videos that were collected from a surveillance camera placed in a real shoe retail store. Results revealed that our approach is sufficiently robust to be used in real world situations and outperforms straightforward CNN approaches. | |||
| Arrangement 1 | urlib.net > SDLA > Fonds > SIBGRAPI 2019 > RetailNet: A deep... | |||
| Arrangement 2 | urlib.net > SDLA > Fonds > Full Index > RetailNet: A deep... | |||
| doc Directory Content | access | |||
| source Directory Content | there are no files | |||
| agreement Directory Content |
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| 4. Conditions of access and use | ||||
| data URL | http://urlib.net/ibi/8JMKD3MGPEW34M/3U2AEMS | |||
| zipped data URL | http://urlib.net/zip/8JMKD3MGPEW34M/3U2AEMS | |||
| Language | en | |||
| Target File | cameraready.pdf | |||
| User Group | thalesv@gmail.com | |||
| Visibility | shown | |||
| Update Permission | not transferred | |||
| 5. Allied materials | ||||
| Mirror Repository | sid.inpe.br/banon/2001/03.30.15.38.24 | |||
| Next Higher Units | 8JMKD3MGPEW34M/3UA4FNL 8JMKD3MGPEW34M/3UA4FPS 8JMKD3MGPEW34M/4742MCS | |||
| Citing Item List | sid.inpe.br/sibgrapi/2019/10.25.18.30.33 2 | |||
| Host Collection | sid.inpe.br/banon/2001/03.30.15.38 | |||
| 6. Notes | ||||
| Empty Fields | archivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume | |||
| 7. Description control | ||||
| e-Mail (login) | thalesv@gmail.com | |||
| update | ||||