A Dataset for User Visual Behaviour with Multi-View Video Content

• Type: Article in International Conference Proceedings Book
• Date: 2023-06

Authors

Maria Teresa Andrade (Author) FEUP; Tiago Soares da Costa (Author) Other; Paula Viana (Author) Other; Nuno Castro Silva (Author) Other

Conference proceedings International

Title: PROCEEDINGS OF THE 2023 PROCEEDINGS OF THE 14TH ACM MULTIMEDIA SYSTEMS CONFERENCE, MMSYS 2023

Pages: 437-443

14th ACM Multimedia Systems Conference (MMSys)

Vancouver, CANADA, JUN 07-10, 2023

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 0 Citations

Other information

Authenticus ID: P-00Y-HH5

DOI: 10.1145/3587819.3592556

Abstract (EN): Immersive video applications impose unpractical bandwidth requirements for best-effort networks. With Multi-View(MV) streaming, these can be minimized by resorting to view prediction techniques. SmoothMV is a multi-view system that uses a non-intrusive head tracking mechanism to detect the viewer's interest and select appropriate views. By coupling Neural Networks (NNs) to anticipate the viewer's interest, a reduction of view-switching latency is likely to be obtained. The objective of this paper is twofold: 1) Present a solution for acquisition of gaze data from users when viewing MV content; 2) Describe a dataset, collected with a large-scale testbed, capable of being used to train NNs to predict the user's viewing interest. Tracking data from head movements was obtained from 45 participants using an Intel Realsense F200 camera, with 7 video playlists, each being viewed a minimum of 17 times. This dataset is publicly available to the research community and constitutes an important contribution to reducing the current scarcity of such data. Tools to obtain saliency/heat maps and generate complementary plots are also provided as an open-source software package.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 7