At present, most unsupervised abnormal behavior detection method only relies on powerful behavior detection classifiers, does not make full use of prior knowledge. This method often has the problem of a huge amount of calculation and affecting the detection speed. In view of the above problems, this paper proposes a weak anomalyreinforced autoencoder for unsupervised anomaly detection method, using U-Net to reconstruct video frames and generative adversarial network to learn the correlation between image entropy and abnormal behavior. Comprehensive experiments on the avenue data set and UCSD data sets verify the effectiveness of our method to detect abnormal events.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.