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      • Department of Computer Engineering
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      What is usual in unusual videos? trajectory snippet histograms for discovering unusualness

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      Author
      İşcen, Ahmet
      Armağan, Anıl
      Duygulu, Pınar
      Date
      2014-06
      Source Title
      IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2014
      Publisher
      IEEE
      Pages
      808 - 813
      Language
      English
      Type
      Conference Paper
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      Abstract
      Unusual events are important as being possible indicators of undesired consequences. Moreover, unusualness in everyday life activities may also be amusing to watch as proven by the popularity of such videos shared in social media. Discovery of unusual events in videos is generally attacked as a problem of finding usual patterns, and then separating the ones that do not resemble them. In this study, we address the problem from a different perspective, and try to answer what type of patterns are shared among unusual videos that make them resemble to each other regardless of the ongoing event. With this challenging problem at hand, we propose a novel descriptor to encode the rapid motions in videos utilizing densely extracted trajectories. The proposed descriptor, which is referred to as trajectory snipped histograms, is used to distinguish unusual videos from usual videos, and further exploited to discover snapshots in which unusualness happen. Experiments on domain specific people falling videos and unrestricted funny videos show the effectiveness of our method in capturing unusualness. © 2014 IEEE.
      Keywords
      Detection
      Event Anomaly
      Computer vision
      Error detection
      Graphic methods
      Trajectories
      Descriptors
      Domain specific
      Event Anomaly
      Rapid motions
      Social media
      Anomaly detection
      Pattern recognition
      Permalink
      http://hdl.handle.net/11693/27286
      Published Version (Please cite this version)
      https://doi.org/10.1109/CVPRW.2014.123
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      • Department of Computer Engineering 1421
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