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Page 24 Xu et al. J Surveill Secur Saf 2020;1:16-33 I http://dx.doi.org/10.20517/jsss.2020.04

algorithm in the experimental data of adding direction noise is 0.0292. This shows that the algorithm has
high positioning accuracy and strong anti-interference ability. It can be seen in Figure 6B that the deviation
generally obeys a right-skewed distribution. Through the above simulation experiments, it can be seen that
the abnormal positioning of a single escape center is highly accurate in the ideal synthetic data experiment.

The optical flow field of the video image is used to perform the acceleration feature extraction to determine
whether an abnormality occurs. If there is an abnormality, there is escape from the center location. The
position and direction information of the acceleration vector obtained when the abnormality is detected is
used to obtain all the acceleration vector reverse extension lines Intersection, and then K nearest neighbor
search is used to determine the possible escape from the center position.

The anomaly localization algorithm in this paper is based on the analysis of the motion vector of the
moving target in the anomaly detection process to locate where the anomaly may occur. The process of
locating is mainly by counting all the intersections of the inverse extension lines of the motion vector, and
using the K nearest neighbor search method to determine the most likely abnormal location. The algorithm
is as follows:
(1) Obtain the image sequence by framing the video, and then extract the motion feature vector.
n
A = a ( ) n ,a ( ) n ,...,a ( ) n }
X { x 1 1 x xk (4)
a
n
A = { y ( ) n 1 ,a ( ) n 1 y ,...,a ( ) n }
Y
yk
(2) Calculate the improved acceleration characteristics for each frame of image, and obtain the improved
acceleration threshold through many experiments. If the improved acceleration value is greater than the
threshold, the acceleration vector of this frame of image is retained.
(3) Calculate the corresponding univariate linear equation parameters according to the acceleration vector
A(a , a ) of the obtained image; ignoring the special case, the parameter expression is as follows:
y
x
k = a y (i,j) / a x (i,j) (5)
l
b = l ik− l × j
where i and j represent the positions of corresponding pixels in the image.
(4) Calculate the intersection point set P = {p , p , … p } according to the straight-line equation of the
1
2
s
acceleration vector. In order for the intersection point set to include all intersection points, the intersection
points of all two different straight lines are mainly calculated. The mathematical derivation is as follows:
x = (b − 2 b 1 ) ( / k − 2 k 1 ) (6)
y = (b − 2 b 1 ) ( / k − 2 k 1 ) k× l + b 1
(5) In the process of calculating the intersection set, there are many repetitions or intersections that are
inconsistent with the actual situation, which are collectively called wild intersections. Therefore, to improve
the accuracy of the algorithm, wild intersections need to be removed from the intersection set.
(6) Determine the escape center by further analyzing the intersection set. Since the intersection set
involved in this article belongs to a simple dataset, the K nearest point search method can be used. The
K nearest point search method is also known as the K nearest neighbor search method. In the process of
determining the escape center, this search method is to calculate the distance between intersections in the
neighborhood, select the Kth smallest distance in the distance set of each intersection, and then compare
them with other intersections to determine if they escaped the center. This search method is suitable for the
classification of rare events. Since the escape center in this section is an infrequent event, and this search
method is easy to implement without training, this paper uses the K nearest neighbor search method to
determine the escape center of the abnormal crowd.

The graphical method of removing wild intersections is mainly based on the number of intersections in
the search window. Intersections are rounded off, which is mainly determined by the particularity of the