6 research outputs found
Automatic Summarization of Soccer Highlights Using Audio-visual Descriptors
Automatic summarization generation of sports video content has been object of
great interest for many years. Although semantic descriptions techniques have
been proposed, many of the approaches still rely on low-level video descriptors
that render quite limited results due to the complexity of the problem and to
the low capability of the descriptors to represent semantic content. In this
paper, a new approach for automatic highlights summarization generation of
soccer videos using audio-visual descriptors is presented. The approach is
based on the segmentation of the video sequence into shots that will be further
analyzed to determine its relevance and interest. Of special interest in the
approach is the use of the audio information that provides additional
robustness to the overall performance of the summarization system. For every
video shot a set of low and mid level audio-visual descriptors are computed and
lately adequately combined in order to obtain different relevance measures
based on empirical knowledge rules. The final summary is generated by selecting
those shots with highest interest according to the specifications of the user
and the results of relevance measures. A variety of results are presented with
real soccer video sequences that prove the validity of the approach
Estudi bibliomètric any 2016. EETAC
El present document recull les publicacions indexades a la base de dades Scopus durant el període comprès entre el mesos de gener a desembre de l’any 2016, escrits per autors pertanyents a l’EETAC. Es presenten les dades recollides segons la font on s’ha publicat, els autors que han publicat, i el tipus de document publicat. S’hi inclou un annex amb la llista de totes les referències bibliogràfiques publicades.Postprint (author's final draft
SoccerDB: A Large-Scale Database for Comprehensive Video Understanding
Soccer videos can serve as a perfect research object for video understanding
because soccer games are played under well-defined rules while complex and
intriguing enough for researchers to study. In this paper, we propose a new
soccer video database named SoccerDB, comprising 171,191 video segments from
346 high-quality soccer games. The database contains 702,096 bounding boxes,
37,709 essential event labels with time boundary and 17,115 highlight
annotations for object detection, action recognition, temporal action
localization, and highlight detection tasks. To our knowledge, it is the
largest database for comprehensive sports video understanding on various
aspects. We further survey a collection of strong baselines on SoccerDB, which
have demonstrated state-of-the-art performances on independent tasks. Our
evaluation suggests that we can benefit significantly when jointly considering
the inner correlations among those tasks. We believe the release of SoccerDB
will tremendously advance researches around comprehensive video understanding.
{\itshape Our dataset and code published on
https://github.com/newsdata/SoccerDB.}Comment: accepted by MM2020 sports worksho
Multimodal framework based on audio‐visual features for summarisation of cricket videos
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/166171/1/ipr2bf02094.pd
Estudi bibliomètric any 2015. EETAC
El present document recull les publicacions indexades a la base de dades Scopus durant el període comprès
entre el mesos de gener a desembre de l’any 2015, escrits per autors pertanyents a l’EETAC. Es presenten les
dades recollides segons la font on s’ha publicat, els autors que han publicat, i el tipus de document publicat.
S’hi inclou un annex amb la llista de totes les referències bibliogràfiques publicades.Postprint (published version
Automatic summarization of soccer highlights using audio-visual descriptors
Automatic summarization generation of sports video content has been object of great interest for many years. Although semantic descriptions techniques have been proposed, many of the approaches still rely on low-level video descriptors that render quite limited results due to the complexity of the problem and to the low capability of the descriptors to represent semantic content. In this paper, a new approach for automatic highlights summarization generation of soccer videos using audio-visual descriptors is presented. The approach is based on the segmentation of the video sequence into shots that will be further analyzed to determine its relevance and interest. Of special interest in the approach is the use of the audio information that provides additional robustness to the overall performance of the summarization system. For every video shot a set of low and mid level audio-visual descriptors are computed and lately adequately combined in order to obtain different relevance measures based on empirical knowledge rules. The final summary is generated by selecting those shots with highest interest according to the specifications of the user and the results of relevance measures. A variety of results are presented with real soccer video sequences that prove the validity of the approach.Peer Reviewe