TY - GEN
T1 - Multimodal Web Based Video Annotator with Real-Time Human Pose Estimation
AU - Rodrigues, Rui
AU - Madeira, Rui Neves
AU - Correia, Nuno
AU - Fernandes, Carla
AU - Ribeiro, Sara
N1 - UID/CCI/04667/2016
CEEC institucional 2
PY - 2019
Y1 - 2019
N2 - This paper presents a multi-platform Web-based video annotator to support multimodal annotation that can be applied to several working areas, such as dance rehearsals, among others. The CultureMoves’ “Motion-Notes” Annotator was designed to assist the creative and exploratory processes of both professional and amateur users, working with a digital device for personal annotations. This prototype is being developed for any device capable of running in a modern Web browser. It is a real-time multimodal video annotator based on keyboard, touch and voice inputs. Five different ways of adding annotations have been already implemented: voice, draw, text, web URL, and mark annotations. Pose estimation functionality uses machine learning techniques to identify a person skeleton in the video frames, which gives the user another resource to identify possible annotations.
AB - This paper presents a multi-platform Web-based video annotator to support multimodal annotation that can be applied to several working areas, such as dance rehearsals, among others. The CultureMoves’ “Motion-Notes” Annotator was designed to assist the creative and exploratory processes of both professional and amateur users, working with a digital device for personal annotations. This prototype is being developed for any device capable of running in a modern Web browser. It is a real-time multimodal video annotator based on keyboard, touch and voice inputs. Five different ways of adding annotations have been already implemented: voice, draw, text, web URL, and mark annotations. Pose estimation functionality uses machine learning techniques to identify a person skeleton in the video frames, which gives the user another resource to identify possible annotations.
KW - Machine learning for creativity
KW - Multimodal video annotations
KW - Real-time human pose estimation
UR - http://www.scopus.com/inward/record.url?scp=85076968658&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-33617-2_3
DO - 10.1007/978-3-030-33617-2_3
M3 - Conference contribution
AN - SCOPUS:85076968658
SN - 9783030336165
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 23
EP - 30
BT - Intelligent Data Engineering and Automated Learning: IDEAL 2019 - 20th International Conference, Proceedings
A2 - Yin, Hujun
A2 - Allmendinger, Richard
A2 - Camacho, David
A2 - Tino, Peter
A2 - Tallón-Ballesteros, Antonio J.
A2 - Menezes, Ronaldo
PB - Springer
T2 - 20th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2019
Y2 - 14 November 2019 through 16 November 2019
ER -