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Xu et al. J Surveill Secur Saf 2020;1:16-33 I http://dx.doi.org/10.20517/jsss.2020.04 Page 19

Figure 1. The algorithm’s detection results are accurate under different block sizes [9]

algorithms achieve high accuracy on public datasets, it is difficult to meet real-time requirements due to,
e.g., computational complexity. Thus, based on data mining, this study combined cloud computing image
processing technology for real-time crime behavior recognition.

3 METHODS
3.1 Population abnormal behavior detection
To analyze the behavior of the crowd, each field is divided into a set of patches (blocks), and the population
density in the image field determines the size of the block for the field. The purpose of this statistical
partitioning of images by patches is mainly to better avoid interference from other factors. The proposed
detection algorithm was tested on the public dataset UMN (University of Minnesota) to find the best block
size. Figure 1 shows that, when the scale is higher than 40, the accuracy becomes larger with the block
size, and the progress is basically stable. When the scale is higher than 40, the accuracy begins to decrease.
However, it should be noted that, if the block size is too large, the motion state cannot be described well.
[10]
Therefore, the block size selected in this section is 24 × 24. The schematic is shown in Figure 2 .

This paper uses a foreground extraction algorithm based on K-means clustering. When the crowd escapes
in the scene, the acceleration of the crowd changes. Therefore, this paper uses the magnitude of the
acceleration to extract the foreground. When the amplitude of a position in the scene is greater than the
threshold, the position is judged as the foreground. In this paper, the K-means algorithm, with k = 2, is
used to calculate the threshold value by randomly calculating the partial vector selected from the sample
[11]
set .

Since the performance of abnormal behaviors is diverse, the definitions of exceptions are different in
different scenarios. For any video image describing the behavior of the crowd, the sequence is described
by the acceleration and velocity vectors. The basic steps of detecting the abnormal escape behavior of the
crowd are as follows: (1) a given video image is smoothed by an image preprocessing method to remove
noise; (2) the velocity and acceleration vectors are calculated using a modified acceleration algorithm for
the processed grayscale image sequence. The improved acceleration algorithm combines the changes in
the characteristics of the movement of the crowd and the changes in the distribution of the population.