This paper describes an evaluation of automatic video summarization systems run on rushes from several BBC dramatic series. It was carried out under the auspices of the TREC Video Retrieval Evaluation (TRECVid) as a followup to the 2007 video summarization workshop held at ACM Multimedia 2007. 31 research teams submitted video summaries of 40 individual rushes video files, aiming to compress out redundant and insignificant material. Each summary had a duration of at most 2% of the original. The output of a baseline system, which simply presented each full video at 50 times normal speed was contributed by Carnegie Mellon University (CMU) as a control.The 2007 procedures for developing ground truth lists of important segments from each video were applied at the National Institute of Standards and Technology (NIST) to the BBC videos. At Dublin City University (DCU) each summary was judged by 3 humans with respect to how much of the ground truth was included and how well-formed the summary was. Additional objective measures included: how long it took the system to create the summary, how long it took the assessor to judge it against the ground truth, and what the summary's duration was. Assessor agreement on finding desired segments averaged 81%. Results indicated that while it was still difficult to exceed the performance of the baseline on in-1 cluding ground truth, the baseline was outperformed by most other systems with respect to avoiding redundancy/junk and presenting the summary with a pleasant tempo/rhythm.
With the widespread use of smartphones as recording devices and the massive growth in bandwidth, the number and volume of video collections has increased significantly in the last years. This poses novel challenges to the management of these large-scale video data and especially to the analysis of and retrieval from such video collections. At the same time, existing video datasets used for research and experimentation are either not large enough to represent current collections or do not reflect the properties of video commonly found on the Internet in terms of content, length, or resolution. In this paper, we introduce the Vimeo Creative Commons Collection, in short V3C, a collection of 28'450 videos (with overall length of about 3'800 hours) published under creative commons license on Vimeo. V3C comes with a shot segmentation for each video, together with the resulting keyframes in original as well as reduced resolution and additional metadata. It is intended to be used from 2019 at the International largescale TREC Video Retrieval Evaluation campaign (TRECVid).
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.