A comprehensive survey of multi-view video summarization

[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.This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019R1A2B5B01070067)Hussain, T.; Muhammad, K.; Ding, W.; Lloret, J.; Baik, SW.; De Albuquerque, VHC. (2021). A comprehensive survey of multi-view video summarization. Pattern Recognition. 109:1-15. https://doi.org/10.1016/j.patcog.2020.10756711510

Perceptual Attributes Optimization for Multivideo Summarization

© 2016 IEEE. Nowadays, many consumer videos are captured by portable devices such as iPhone. Different from constrained videos that are produced by professionals, e.g., those for broadcast, summarizing multiple handheld videos from a same scenery is a challenging task. This is because: 1) these videos have dramatic semantic and style variances, making it difficult to extract the representative key frames; 2) the handheld videos are with different degrees of shakiness, but existing summarization techniques cannot alleviate this problem adaptively; and 3) it is difficult to develop a quality model that evaluates a video summary, due to the subjectiveness of video quality assessment. To solve these problems, we propose perceptual multiattribute optimization which jointly refines multiple perceptual attributes (i.e., video aesthetics, coherence, and stability) in a multivideo summarization process. In particular, a weakly supervised learning framework is designed to discover the semantically important regions in each frame. Then, a few key frames are selected based on their contributions to cover the multivideo semantics. Thereafter, a probabilistic model is proposed to dynamically fit the key frames into an aesthetically pleasing video summary, wherein its frames are stabilized adaptively. Experiments on consumer videos taken from sceneries throughout the world demonstrate the descriptiveness, aesthetics, coherence, and stability of the generated summary