InSocialNet: Interactive visual analytics for role-event videos

Pan, Yaohua, Niu, Zhibin, Wu, Jing ORCID: https://orcid.org/0000-0001-5123-9861 and Zhang, Jiawan 2019. InSocialNet: Interactive visual analytics for role-event videos. Computational Visual Media 5 (4) , pp. 375-390. 10.1007/s41095-019-0157-9

Preview

PDF - Accepted Post-Print Version Available under License Creative Commons Attribution. Download (2MB) | Preview

Abstract

Role–event videos are rich in information but challenging to be understood at the story level. The social roles and behavior patterns of characters largely depend on the interactions among characters and the background events. Understanding them requires analysis of the video contents for a long duration, which is beyond the ability of current algorithms designed for analyzing short-time dynamics. In this paper, we propose InSocialNet, an interactive video analytics tool for analyzing the contents of role–event videos. It automatically and dynamically constructs social networks from role–event videos making use of face and expression recognition, and provides a visual interface for interactive analysis of video contents. Together with social network analysis at the back end, InSocialNet supports users to investigate characters, their relationships, social roles, factions, and events in the input video. We conduct case studies to demonstrate the effectiveness of InSocialNet in assisting the harvest of rich information from role–event videos. We believe the current prototype implementation can be extended to applications beyond movie analysis, e.g., social psychology experiments to help understand crowd social behaviors.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Springer
ISSN:	2096-0433
Date of First Compliant Deposit:	23 January 2020
Date of Acceptance:	24 December 2019
Last Modified:	05 May 2023 09:06
URI:	https://orca.cardiff.ac.uk/id/eprint/128961

Citation Data

Cited 2 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item