@InProceedings{MatosNasc:2022:MuApAc,
author = "Matos, Diognei and Nascimento, Erickson R.",
affiliation = "{Federal University of Minas Gerais} and {Federal University of
Minas Gerais}",
title = "Musical Hyperlapse: A Multimodal Approach to Accelerate
First-Person Videos",
booktitle = "Proceedings...",
year = "2022",
organization = "Conference on Graphics, Patterns and Images, 35. (SIBGRAPI)",
keywords = "computer vision, music emotion recognition, image emotion
recognition, semantic hyperlapse.",
abstract = "With the advance in technology and social media usage,
first-person recording videos has become a common habit. These
videos are usually very long and tiring to watch, bringing the
need to speed up them. Despite recent progress of fast-forward
methods, they do not consider inserting background music in the
videos, which could make them more enjoyable. This thesis presents
a new method that creates accelerated videos and includes the
background music keeping the same emotion induced by visual and
acoustic modalities. Our approach is based on the automatic
recognition of emotions induced by music and video contents and an
optimization algorithm that maximizes the visual quality of the
output video and seeks to match the similarity of the music and
the video's emotions. Quantitative results show that our method
achieves the best performance in matching emotion similarity while
maintaining the visual quality of the output video compared with
other literature methods. Visual results can be seen through the
link: https://youtu.be/9ykQa9zhcz8.",
conference-location = "Natal, RN",
conference-year = "24-27 Oct. 2022",
language = "en",
ibi = "8JMKD3MGPEW34M/47QSJL5",
url = "http://urlib.net/ibi/8JMKD3MGPEW34M/47QSJL5",
targetfile = "Musical Hyperlapse WTD Paper.pdf",
urlaccessdate = "2024, May 19"
}