Detecting Irregularities in Images and in Video |
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Authors: | Oren Boiman Michal Irani |
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Affiliation: | (1) Department of Computer Science and Applied Math, The Weizmann Institute of Science, 76100 Rehovot, Israel |
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Abstract: | We address the problem of detecting irregularities in visual data, e.g., detecting suspicious behaviors in video sequences,
or identifying salient patterns in images. The term “irregular” depends on the context in which the “regular” or “valid” are
defined. Yet, it is not realistic to expect explicit definition of all possible valid configurations for a given context.
We pose the problem of determining the validity of visual data as a process of constructing a puzzle: We try to compose a
new observed image region or a new video segment (“the query”) using chunks of data (“pieces of puzzle”) extracted from previous
visual examples (“the database”). Regions in the observed data which can be composed using large contiguous chunks of data
from the database are considered very likely, whereas regions in the observed data which cannot be composed from the database
(or can be composed, but only using small fragmented pieces) are regarded as unlikely/suspicious. The problem is posed as
an inference process in a probabilistic graphical model. We show applications of this approach to identifying saliency in
images and video, for detecting suspicious behaviors and for automatic visual inspection for quality assurance.
Patent Pending |
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Keywords: | detecting suspicious behaviors saliency detecting irregularities novelty detection anomaly detection action recognition automatic visual inspection |
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