Data-Driven Crowd Understanding: A Baseline for a Large-Scale Crowd Dataset
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AbstractCrowd understanding has drawn increasing attention from the computer vision community, and its progress is driven by the availability of public crowd datasets. In this paper, we contribute a large-scale benchmark dataset collected from the Shanghai 2010 World Expo. It includes 2630 annotated video sequences captured by 245 surveillance cameras, far larger than any public dataset. It covers a large number of different scenes and is suitable for evaluating the performance of crowd segmentation and estimation of crowd density, collectiveness, and cohesiveness, all of which are universal properties of crowd systems. In total, 53 637 crowd segments are manually annotated with the three crowd properties. This dataset is released to the public to advance research on crowd understanding. The large-scale annotated dataset enables using data-driven approaches for crowd understanding. In this paper, a data-driven approach is proposed as a baseline of crowd segmentation and estimation of crowd properties for the proposed dataset. Novel global and local crowd features are designed to retrieve similar training scenes and to match spatio-temporal crowd patches so that the labels of the training scenes can be accurately transferred to the query image. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art approaches for crowd understanding.
All Author(s) ListZhang C, Kang K, Li HS, Wang XG, Xie R, Yang XK
Journal nameIEEE Transactions on Multimedia
Volume Number18
Issue Number6
Pages1048 - 1061
LanguagesEnglish-United Kingdom
KeywordsCrowd features; crowd scene understanding; data-driven methods; large-scale benchmark
Web of Science Subject CategoriesComputer Science; Computer Science, Information Systems; Computer Science, Software Engineering; Telecommunications

Last updated on 2020-25-10 at 01:55