Appearance-Motion Memory Consistency Network for Video Anomaly Detection.
2021
Abnormal event detection in the surveillance video is an essential
but challenging task, and many methods have been
proposed to deal with this problem. The previous methods
either only consider the appearance information or directly
integrate the results of appearance and motion information
without considering their endogenous consistency
semantics explicitly. Inspired by the rule humans identify
the abnormal frames from multi-modality signals, we propose
an Appearance-Motion Memory Consistency Network
(AMMC-Net). Our method first makes full use of the prior
knowledge of appearance and motion signals to explicitly
capture the correspondence between them in the high-level
feature space. Then, it combines the multi-view features to
obtain a more essential and robust feature representation of
regular events, which can significantly increase the gap between
an abnormal and a regular event. In the anomaly detection
phase, we further introduce a commit error in the latent
space joint with the prediction error in pixel space to enhance
the detection accuracy. Solid experimental results on various
standard datasets validate the effectiveness of our approach.
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