Fast forwarding Egocentric Videos by Listening and Watching
This work addresses the issue of tedious first-person videos for users by providing an incremental audio-based method to improve fast-forwarding.
The authors tackled the problem of fast-forwarding long egocentric videos by using psychoacoustic metrics from the soundtrack to emphasize pleasant moments, achieving demonstrated efficiency in speed-up and instability metrics.
The remarkable technological advance in well-equipped wearable devices is pushing an increasing production of long first-person videos. However, since most of these videos have long and tedious parts, they are forgotten or never seen. Despite a large number of techniques proposed to fast-forward these videos by highlighting relevant moments, most of them are image based only. Most of these techniques disregard other relevant sensors present in the current devices such as high-definition microphones. In this work, we propose a new approach to fast-forward videos using psychoacoustic metrics extracted from the soundtrack. These metrics can be used to estimate the annoyance of a segment allowing our method to emphasize moments of sound pleasantness. The efficiency of our method is demonstrated through qualitative results and quantitative results as far as of speed-up and instability are concerned.