6533b832fe1ef96bd129a371
RESEARCH PRODUCT
A comprehensive survey of multi-view video summarization
Weiping DingKhan MuhammadSung Wook BaikTanveer HussainVictor Hugo C. De AlbuquerqueJaime Lloretsubject
Multi-sensor managementComputer scienceFeature extraction02 engineering and technologycomputer.software_genre01 natural sciencesAutomatic summarizationFeatures fusionBig dataRedundancy (information theory)Multi-camera networksArtificial IntelligenceMulti-view video summarization0103 physical sciencesSignal ProcessingMachine learning0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingComputer visionComputer Vision and Pattern RecognitionData mining010306 general physicscomputerVideo summarization surveySoftwaredescription
[EN] There has been an exponential growth in the amount of visual data on a daily basis acquired from single or multi-view surveillance camera networks. This massive amount of data requires efficient mechanisms such as video summarization to ensure that only significant data are reported and the redundancy is reduced. Multi-view video summarization (MVS) is a less redundant and more concise way of providing information from the video content of all the cameras in the form of either keyframes or video segments. This paper presents an overview of the existing strategies proposed for MVS, including their advantages and drawbacks. Our survey covers the genericsteps in MVS, such as the pre-processing of video data, feature extraction, and post-processing followed by summary generation. We also describe the datasets that are available for the evaluation of MVS. Finally, we examine the major current issues related to MVS and put forward the recommendations for future research(1). (C) 2020 Elsevier Ltd. All rights reserved.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2021-01-01 |