Patent ID: 11875566
Assignee: THE FLORIDA INTERNATIONAL UNIVERSITY BOARD OF TRUSTEES
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 19:
20. A system for detecting one or more anomalous events in video data, the system comprising:
a processor;
a display; and
a machine-readable medium in operable communication with the display and the processor and having instructions stored thereon that, when executed by the processor, perform the following steps:
a) receiving the video data;
b) performing a noise cleansing on the video data to provide cleansed video data;
c) performing feature extraction on the cleansed video data using a neural network to give feature-extracted video data;
d) performing instance segmentation on the feature-extracted video data to give a plurality of segmented instances, each segmented instance of the plurality of segmented instances representing a predetermined amount of time of the feature-extracted video data;
e) performing instance summation on the plurality of segmented instances by calculating a sum of extracted feature values on each segmented instance of the plurality of segmented instances, to give a plurality of segmented instance sums;
f) performing instance difference calculation on the plurality of segmented instance sums to determine a plurality of difference values for the plurality of segmented instance sums, respectively;
g) normalizing the plurality of difference values to give a plurality of normalized difference values, each normalized difference value of the plurality of normalized difference values representing how different each instance is to at least one adjacent instance;
h) if the normalized difference value for a given instance is greater than a predetermined threshold, marking the given instance as an anomalous event; and
i) displaying any detected anomalous event on the display for a user to review and verify,
the predetermined amount of time being one second,
the noise cleansing being a histogram-based noise cleansing,
the performing of the feature extraction comprising using a pretrained model that is a convolutional neural network that is 16 layers deep,
the instructions when executed performing no feature classification step, and
the video data being surveillance video data from at least one surveillance camera.