Surveillance using both video and audio

buir.contributor.authorÇetin, A. Enis
buir.contributor.authorGüdükbay, Uğur
buir.contributor.orcidÇetin, A. Enis|0000-0002-3449-1958
dc.citation.epage155en_US
dc.citation.spage143en_US
dc.contributor.authorDedeoğlu, Yiğithanen_US
dc.contributor.authorTöreyin, B. Uğuren_US
dc.contributor.authorGüdükbay, Uğuren_US
dc.contributor.authorÇetin, A. Enisen_US
dc.contributor.editorMaragos, P.
dc.contributor.editorPotamianos, A.
dc.contributor.editorGros, P.
dc.date.accessioned2019-04-25T11:32:13Z
dc.date.available2019-04-25T11:32:13Z
dc.date.issued2008en_US
dc.departmentDepartment of Computer Engineeringen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.descriptionChapter 6
dc.description.abstractIt is now possible to install cameras monitoring sensitive areas but it may not be possible to assign a security guard to each camera or a set of cameras. In addition, security guards may get tired and watch the monitor in a blank manner without noticing important events taking place in front of their eyes. Current CCTV surveillance systems are mostly based on video and recently intelligent video analysis systems capable of detecting humans and cars were developed for surveillance applications. Such systems mostly use Hidden Markov Models (HMM) or Support Vector Machines (SVM) to reach decisions. They detect important events but they also produce false alarms. It is possible to take advantage of other low cost sensors including audio to reduce the number of false alarms. Most video recording systems have the capability of recording audio as well. Analysis of audio for intelligent information extraction is a relatively new area. Automatic detection of broken glass sounds, car crash sounds, screams, increasing sound level at the background are indicators of important events. By combining the information coming from the audio channel with the information from the video channels, reliable surveillance systems can be built. In this chapter, current state of the art is reviewed and an intelligent surveillance system analyzing both audio and video channels is described.en_US
dc.description.provenanceSubmitted by Zeynep Aykut (zeynepay@bilkent.edu.tr) on 2019-04-25T11:32:13Z No. of bitstreams: 1 Surveillance_using_both_video_and_audio.pdf: 513139 bytes, checksum: 8ec9a41a2637100ad9e1f97c75448268 (MD5)en
dc.description.provenanceMade available in DSpace on 2019-04-25T11:32:13Z (GMT). No. of bitstreams: 1 Surveillance_using_both_video_and_audio.pdf: 513139 bytes, checksum: 8ec9a41a2637100ad9e1f97c75448268 (MD5) Previous issue date: 2008en
dc.identifier.doi10.1007/978-0-387-76316-3_6en_US
dc.identifier.doi10.1007/978-0-387-76316-3en_US
dc.identifier.eisbn9780387763163
dc.identifier.isbn9780387763156
dc.identifier.urihttp://hdl.handle.net/11693/50941
dc.language.isoEnglishen_US
dc.publisherSpringeren_US
dc.relation.ispartofMultimodal processing and interaction: audio, video, texten_US
dc.relation.ispartofseriesMultimedia Systems and Applications;33
dc.relation.isversionofhttps://doi.org/10.1007/978-0-387-76316-3_6en_US
dc.relation.isversionofhttps://doi.org/10.1007/978-0-387-76316-3en_US
dc.subjectSupport vector machineen_US
dc.subjectDiscrete cosine transformen_US
dc.subjectAudio signalen_US
dc.subjectSupport vector machine modelen_US
dc.subjectHuman groupen_US
dc.titleSurveillance using both video and audioen_US
dc.typeBook Chapteren_US

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