文件名称:Real-Time-Abnormality-Detection-fromWebcams
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This paperpresent a data-driven, unsupervised method for unusual
scene detection from static webcams. Such time-lapse
data is usually captured with very low or varying fr a merate.
This precludes the use of tools typically used in
surveillance (e.g., object tracking). Hence, our algorithm
is based on simple image features. This paperdefine usual scenes
based on the concept of meaningful nearest neighbours instead
of building explicit models. To effectively compare
the observations, our algorithm adapts the data representation.
Furthermore, This paperuse incremental learning techniques
to adapt to changes in the data-stream. Experiments on several
months of webcam data show that our approach detects
plausible unusual scenes, which have not been observed in
the data-stream before.
scene detection from static webcams. Such time-lapse
data is usually captured with very low or varying fr a merate.
This precludes the use of tools typically used in
surveillance (e.g., object tracking). Hence, our algorithm
is based on simple image features. This paperdefine usual scenes
based on the concept of meaningful nearest neighbours instead
of building explicit models. To effectively compare
the observations, our algorithm adapts the data representation.
Furthermore, This paperuse incremental learning techniques
to adapt to changes in the data-stream. Experiments on several
months of webcam data show that our approach detects
plausible unusual scenes, which have not been observed in
the data-stream before.
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Hunting Nessie ? Real-Time Abnormality Detection fromWebcams.pdf