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Spott : on-the-spot e-commerce for television using deep learning-based video analysis techniques

Florian Vandecasteele UGent, Karel Vandenbroucke, Dimitri Schuurman UGent and Steven Verstockt UGent (2017) ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS. 13(3).
abstract
Spott is an innovative second screen mobile multimedia application which offers viewers relevant information on objects (e.g., clothing, furniture, food) they see and like on their television screens. The application enables interaction between TV audiences and brands, so producers and advertisers can offer potential consumers tailored promotions, e-shop items, and/or free samples. In line with the current views on innovation management, the technological excellence of the Spott application is coupled with iterative user involvement throughout the entire development process. This article discusses both of these aspects and how they impact each other. First, we focus on the technological building blocks that facilitate the (semi-) automatic interactive tagging process of objects in the video streams. The majority of these building blocks extensively make use of novel and state-of-the-art deep learning concepts and methodologies. We show how these deep learning based video analysis techniques facilitate video summarization, semantic keyframe clustering, and (similar) object retrieval. Secondly, we provide insights in user tests that have been performed to evaluate and optimize the application's user experience. The lessons learned from these open field tests have already been an essential input in the technology development and will further shape the future modifications to the Spott application.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
keyword
Interactive television, video summarization, deep learning, object, recognition, metadata enrichment, experience studies, user-validation
journal title
ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
ACM Trans. Multimed. Comput. Commun. Appl.
volume
13
issue
3
article number
38
pages
16 pages
publisher
Assoc Computing Machinery
place of publication
New york
Web of Science type
Article
Web of Science id
000417400400006
ISSN
1551-6857
1551-6865
DOI
10.1145/3092834
language
English
UGent publication?
yes
classification
A1
id
8544008
handle
http://hdl.handle.net/1854/LU-8544008
date created
2018-01-08 09:37:14
date last changed
2018-01-15 10:16:09
@article{8544008,
  abstract     = {Spott is an innovative second screen mobile multimedia application which offers viewers relevant information on objects (e.g., clothing, furniture, food) they see and like on their television screens. The application enables interaction between TV audiences and brands, so producers and advertisers can offer potential consumers tailored promotions, e-shop items, and/or free samples. In line with the current views on innovation management, the technological excellence of the Spott application is coupled with iterative user involvement throughout the entire development process. This article discusses both of these aspects and how they impact each other. First, we focus on the technological building blocks that facilitate the (semi-) automatic interactive tagging process of objects in the video streams. The majority of these building blocks extensively make use of novel and state-of-the-art deep learning concepts and methodologies. We show how these deep learning based video analysis techniques facilitate video summarization, semantic keyframe clustering, and (similar) object retrieval. Secondly, we provide insights in user tests that have been performed to evaluate and optimize the application's user experience. The lessons learned from these open field tests have already been an essential input in the technology development and will further shape the future modifications to the Spott application.},
  articleno    = {38},
  author       = {Vandecasteele, Florian and Vandenbroucke, Karel and Schuurman, Dimitri and Verstockt, Steven},
  issn         = {1551-6857},
  journal      = {ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS},
  keyword      = {Interactive television,video summarization,deep learning,object,recognition,metadata enrichment,experience studies,user-validation},
  language     = {eng},
  number       = {3},
  pages        = {16},
  publisher    = {Assoc Computing Machinery},
  title        = {Spott : on-the-spot e-commerce for television using deep learning-based video analysis techniques},
  url          = {http://dx.doi.org/10.1145/3092834},
  volume       = {13},
  year         = {2017},
}

Chicago
Vandecasteele, Florian, Karel Vandenbroucke, Dimitri Schuurman, and Steven Verstockt. 2017. “Spott : On-the-spot E-commerce for Television Using Deep Learning-based Video Analysis Techniques.” Acm Transactions on Multimedia Computing Communications and Applications 13 (3).
APA
Vandecasteele, F., Vandenbroucke, K., Schuurman, D., & Verstockt, S. (2017). Spott : on-the-spot e-commerce for television using deep learning-based video analysis techniques. ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS, 13(3).
Vancouver
1.
Vandecasteele F, Vandenbroucke K, Schuurman D, Verstockt S. Spott : on-the-spot e-commerce for television using deep learning-based video analysis techniques. ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS. New york: Assoc Computing Machinery; 2017;13(3).
MLA
Vandecasteele, Florian, Karel Vandenbroucke, Dimitri Schuurman, et al. “Spott : On-the-spot E-commerce for Television Using Deep Learning-based Video Analysis Techniques.” ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 13.3 (2017): n. pag. Print.