Saliency in crowd

Research output: Chapter in Book/Report/Conference proceedingConference contribution

44 Scopus citations


Theories and models on saliency that predict where people look at focus on regular-density scenes. A crowded scene is characterized by the co-occurrence of a relatively large number of regions/objects that would have stood out if in a regular scene, and what drives attention in crowd can be significantly different from the conclusions in the regular setting. This work presents a first focused study on saliency in crowd. To facilitate saliency in crowd study, a new dataset of 500 images is constructed with eye tracking data from 16 viewers and annotation data on faces (the dataset will be publicly available with the paper). Statistical analyses point to key observations on features and mechanisms of saliency in scenes with different crowd levels and provide insights as of whether conventional saliency models hold in crowding scenes. Finally a new model for saliency prediction that takes into account the crowding information is proposed, and multiple kernel learning (MKL) is used as a core computational module to integrate various features at both low- and high-levels. Extensive experiments demonstrate the superior performance of the proposed model compared with the state-of-the-art in saliency computation.

Original languageEnglish (US)
Title of host publicationComputer Vision, ECCV 2014 - 13th European Conference, Proceedings
PublisherSpringer Verlag
Number of pages16
EditionPART 7
ISBN (Print)9783319105833
StatePublished - 2014
Event13th European Conference on Computer Vision, ECCV 2014 - Zurich, Switzerland
Duration: Sep 6 2014Sep 12 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 7
Volume8695 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other13th European Conference on Computer Vision, ECCV 2014


  • crowd
  • multiple kernel learning
  • saliency
  • visual attention


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