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Page 22 Xu et al. J Surveill Secur Saf 2020;1:16-33 I http://dx.doi.org/10.20517/jsss.2020.04

Figure 4. Block diagram of the principle of graphic method

accuracy of the algorithm, it is necessary to remove wild intersection points from the intersection point set;
and (6) by further analyzing the intersection set, the escape center is determined. Since the intersection set
referred to in this paper 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 calculates the distance between the intersections in
the neighborhood, selects the Kth minimum distance in the distance set of each intersection, and compares
it with other intersections to determine whether it is the escape 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
[19]
to determine the escape center of abnormal populations .

In determining the escape center, the determination of the intersection of the lines of the improved
acceleration vector in the image plays a very important role in the overall algorithm. In the process of
determining the intersection, all intersections are calculated and the wild intersections are removed.
The calculation method of the intersection point is introduced above, and the removal process of the
intersection point is introduced here.

The wild intersection removal method in this paper adopts the graphical removal method, as shown in
[20]
Figure 4. The specific steps of the algorithm are as follows : (1) the intersection set P = {p , p , … p }
s
2
1
solved by the improved acceleration vector is taken as an input; (2) the overall scope of the graphical search
is determined; and (3) a small search window is designed to count the number of intersections in the
search range determined in the previous step. The size of the small search window is determined before the
start of the detection and is mainly determined according to the number of moving objects in the image
and the size of the image.

The graphic method removes the wild intersection points mainly based on the number of intersection
points in the search window to make the intersection point, which is mainly determined by the
particularity of the research object of the algorithm. Moreover, the algorithm is mainly used to locate the