Patent AU-2021218192-B1 (AU)
Title: Video anomaly detection method and system based on generative assistant discriminative network
Inventor: LI HONGJUN
IPC: G06N20/00
Filed: 2021-08-20
Abstract: OF THE DISCLOSURE 
The present disclosure relates to a video anomaly detection method and system based on a 
generative assistant discriminative network, where the method includes: collecting and 
converting normal video surveillance data into original frames, selecting an original future frame, 
and adding noise to the original future frame to obtain a noisy future frame; inputting the original 
frames into a generator, to obtain a predicted future frame; calculating optical flow information 
between the predicted future frame and a previous frame of the original future frame; calculating 
optical flow information between the original future frame and the previous frame of the original 
future frame, calculating a difference between the two piece of optical flow information, inputting 
the predicted future frame and the original future frame into a discriminator, and constructing an 
objective function of the discriminator; inputting the noisy future frame and the predicted future 
frame into an assistor, and constructing an objective function of the assistor; constructing an 
objective function of the generator; updating the generator, and determining a predicted future 
frame during testing; and calculating an anomaly score, and determining, according to the 
anomaly score, whether a frame to be tested is abnormal. The foregoing method in the present 
disclosure can improve the accuracy of detection of abnormal events in a video.  
ABSTRACT DRAWING 
Collecting normal video surveillance data 101 
Converting the video surveillance data into original frames P02 
Selecting a t' frame from the original frames as an original future frame I3 
Adding noise to the original future frame to obtain a noisy future frame 104 
inputting the orgial ames into a generator, so that the generator learnsspatiaan 
temporal features according to the first successive frames, to obtain a predictedfutre 
me 105 
Calculating optical flow information between the predicted future frame and a previous 
frame ofthe original future frame, denoted as first optical flow information 
106 
Calculating optical flow information between the original future frame and the previous 
frame ofthe original future frame, denoted as second optical flow information 
Calculatingadifferencebetween the first optical flow information and the second optical 
flow information 10 
Inputting the predicted future frame and the original future frame into a discriminator, to 
obtain a probability that the predicted future frame belongs to the original frames and a 109 
probability that the original future frame belongs to the original frames 
Constructinganobjective function of the discriminator according to the probability 1 
D(l) and the probability D(T) 10 
Inputting the noisy future frame and the predicted future frame into an assistor, to obtain a 
probability that the noisy future frame belongs to normal future frames and a probability 
that the predicted future frame belongs to the nonnal future frames il 
Constructing an objective function of the assistor according to the probabilityA( and 1 
the probability A(I) 112 
Feeding back the difference between the first optical flow information and the second 
optical flow information, the probabilityD(,), and the probabilityA(I) to the generator for 113 
adversarial training with the discriminator, and for cooperative training with the assistor to 
optimize the generator, and constructing an objective function of the generator 
Contnuously updating the generator, the iscrimmator, and the assistor n an terative 
process based on the objective function ofthe discriminator, the objective function ofthe 14 
assistor, and the objective function ofthe generator, until none ofthe generator, the 
discriminator, and the assistor can be improved 
Determining a predicted future frame during testing based on the updatedgenerator 115 
Calculating a peak signal-to-noise ratio of the predicted future frame and the original future 6 
frame 
Calculating an anomaly score based on the peak signal-to-noise ratio 117 
Determining according to the anomaly score, whether a frame to be tested is abnormal 118 
FIG I