Large margin high-order deep learning with auxiliary tasks for video-based anomaly detection

A computer program product for video-based anomaly detection, the computer program product comprising a non-transitory computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform a method comprising:

• forming, by a processor, a Deep High-Order Convolutional Neural Network (DHOCNN)-based model having a one-class Support Vector Machine (SVM) as a loss layer of the DHOCNN-based model, an objective of the SVM being configured to perform the video-based anomaly detection;
• generating, by the processor, one or more predictions of an impending anomaly based on the high-order deep learning based model applied to an input image;
• and
• initiating, by the processor, an action to a hardware device to mitigate expected harm to at least one item selected from the group consisting of the hardware device, another hardware device related to the hardware device, and a person related to the hardware device.

A computer processing system for performing video-based anomaly detection, comprising:

• a processor, configured to:
• form a Deep High-Order Convolutional Neural Network (DHOCNN)-based model having a one-class Support Vector Machine (SVM) as a loss layer of the DHOCNN-based model, an objective of the SVM being configured to perform the video-based anomaly detection;
• generate one or more predictions of an impending anomaly based on the high-order deep learning based model applied to an input image;
• and
• initiate an action to a hardware device to mitigate expected harm to at least one item selected from the group consisting of the hardware device, another hardware device related to the hardware device, and a person related to the hardware device.