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dc.contributor.authorStauffer, Chris
dc.date.accessioned2005-12-22T02:36:45Z
dc.date.available2005-12-22T02:36:45Z
dc.date.issued2005-09-20
dc.identifier.otherMIT-CSAIL-TR-2005-057
dc.identifier.otherAIM-2005-026
dc.identifier.urihttp://hdl.handle.net/1721.1/30568
dc.description.abstractCurrent computer vision techniques can effectively monitor gross activities in sparse environments. Unfortunately, visual stimulus is often not sufficient for reliably discriminating between many types of activity. In many cases where the visual information required for a particular task is extremely subtle or non-existent, there is often audio stimulus that is extremely salient for a particular classification or anomaly detection task. Unfortunately unlike visual events, independent sounds are often very ambiguous and not sufficient to define useful events themselves. Without an effective method of learning causally-linked temporal sequences of sound events that are coupled to the visual events, these sound events are generally only useful for independent anomalous sounds detection, e.g., detecting a gunshot or breaking glass. This paper outlines a method for automatically detecting a set of audio events and visual events in a particular environment, for determining statistical anomalies, for automatically clustering these detected events into meaningful clusters, and for learning salient temporal relationships between the audio and visual events. This results in a compact description of the different types of compound audio-visual events in an environment.
dc.format.extent9 p.
dc.format.extent32903979 bytes
dc.format.extent1153580 bytes
dc.format.mimetypeapplication/postscript
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.relation.ispartofseriesMassachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory
dc.subjectAI
dc.subjectUnsupervised
dc.subjectactivity analysis
dc.subjectscene modeling
dc.subjecttracking
dc.subjectevent detection
dc.titleAutomated Audio-visual Activity Analysis


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