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Page 28 Xu et al. J Surveill Secur Saf 2020;1:16-33 I http://dx.doi.org/10.20517/jsss.2020.04

A B C

Figure 11. Results of escaping from the center position in the synthesized vector field. A: one escape center; B: two escape centers; C:
three escape centers

As shown in Figure 11, the algorithm was used to perform different positioning experiments on different
numbers of escape centers. Considering that it is difficult to have more than three sudden escape events in
actual scenes, the research situation was a simple incident.

Since the data used in the simulation experiment were specifically designed, the red mark and green
mark in Figure 11, respectively, represent the detected escape center and the ideally preset escape center.
The detection of ideal data can verify that the algorithm is theoretically feasible. To make the designed
data closer to an actual dataset, the same method as the previous simulation experiment to add Gaussian
direction noise was chosen. It was found through experiments that the addition of directional noise did
not affect the accuracy of the algorithm much. However, from the above simulation experiments, it can be
seen that, for the three scenarios of escaping the center, the selection of K value by the KNN search method
during the process of escaping the center and removing the wild intersections has a certain impact on the
performance of the entire algorithm. These two factors that may affect the performance of the algorithm
were analyzed further, as shown in Figures 12-14.

As shown in Figure 12, the overall positioning performance of the algorithm is still very high. Figures
13 and 14 show the change of the mean square error of the positioning error caused by the selection of
different K values during the algorithm’s search process for escaping the center and the process of removing
wild points. It can be seen from the fitting curve in Figure 13 that the accuracy of selecting an appropriate
K-value algorithm corresponding to different directional noise angles can be higher. Figure 14 is a
comparison diagram between the searching wild intersection removal method and the graphical method.
It can be seen in the figure that properly selecting the value of K under a fixed noise angle can make the
performance of the algorithm in this section better.

In order to test the effect of this research algorithm in the actual crime detection, through the intelligent
identification of 60 sets of crime surveillance videos, the identification effect of crime actions is counted.
The crime recognition accuracy rate is shown in Table 4 and Figure 15.

As shown in Figure 15, this algorithm has a high recognition rate for criminal actions, and has certain
practical significance. It can be applied to practice.

5 DISCUSSION
This study verified the accuracy of the abnormal positioning method of the single escape center through
simulation experiments. First, this study verified the accuracy of positioning under synthetic data, which
mainly proved the feasibility of the theory. Then, this study validated the positioning method using