People Counting in Videos by Fusing Temporal Cues from Spatial Context-Aware Convolutional Neural Networks

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dc.contributor.author Sourtzinos, Panos
dc.contributor.author Velastin Carroza, Sergio Alejandro
dc.contributor.author Jara, Miguel
dc.contributor.author Zegers, Pablo
dc.contributor.author Makris, Dimitrios
dc.date.accessioned 2019-10-02T07:36:48Z
dc.date.available 2019-10-02T07:36:48Z
dc.date.issued 2016-11-03
dc.identifier.bibliographicCitation Sourtzinos, P., Velastin, S.A., Jara, M., Zegers, P. y Makris, D. (2016). People Counting in Videos by Fusing Temporal Cues from Spatial Context-Aware Convolutional Neural Networks. In European Conference on Computer Vision 2016 Workshops, Part II, LNCS 9914, pp. 655–667.
dc.identifier.isbn 978-3-319-48880-6
dc.identifier.uri http://hdl.handle.net/10016/28944
dc.description This paper has been presented at : 14th European Conference on Computer Vision
dc.description.abstract We present an efficient method for people counting in video sequences from fixed cameras by utilising the responses of spatially context-aware convolutional neural networks (CNN) in the temporal domain. For stationary cameras, the background information remains fairly static, while foreground characteristics, such as size and orientation may depend on their image location, thus the use of whole frames for training a CNN improves the differentiation between background and foreground pixels. Foreground density representing the presence of people in the environment can then be associated with people counts. Moreover the fusion, of the responses of count estimations, in the temporal domain, can further enhance the accuracy of the final count. Our methodology was tested using the publicly available Mall dataset and achieved a mean deviation error of 0.091.
dc.format.extent 13
dc.language.iso eng
dc.publisher Springer
dc.rights © Springer International Publishing Switzerland 2016
dc.subject.other People counting
dc.subject.other Convolutional neural networks
dc.subject.other Video analysis
dc.title People Counting in Videos by Fusing Temporal Cues from Spatial Context-Aware Convolutional Neural Networks
dc.type bookPart
dc.type conferenceObject
dc.subject.eciencia Informática
dc.identifier.doi https://doi.org/10.1007/978-3-319-48881-3_46
dc.rights.accessRights openAccess
dc.type.version acceptedVersion
dc.relation.eventdate 08-16 October 2016
dc.relation.eventplace Amsterdam, The Netherlands
dc.relation.eventtitle 14th European Conference on Computer Vision 2016 Workshop
dc.relation.eventtype proceeding
dc.identifier.publicationfirstpage 655
dc.identifier.publicationlastpage 667
dc.identifier.publicationtitle Computer Vision - ECCV 2016 Workshops
dc.identifier.publicationvolume 9914
dc.identifier.uxxi CC/0000029988
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