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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 17

1 INTRODUCTION
Current crime behavior observation has the problem of not being real time, thus criminal behavior cannot
be promptly controlled. To improve the control of criminal behavior, this study was based on cloud
computing image processing, and adopted data mining for criminal behavior. his study obtained many
criminal behavior characteristics through data collection and combined the rapid response capability of
cloud computing to adopt data processing.

Criminal behavior has always affected the stability of societies, and it is difficult to control crimes in real
time through monitoring and observation. In recent years, with the continuous advancement of society,
the level of material and spiritual living has continuously improved, and people have begun to pay more
attention to the safety of their lives and property. As a settlement of the population, the city has a more
urgent need to ensure that life and property are not invaded. Due to the high population density and
complex personnel structure, urban management becomes more and more difficult, and various public
security incidents are more likely to occur. How to effectively control crimes is an important research
content of current social management, and the analysis of big data crime behavior is an effective way.

The abnormality detection object in this paper is a random group in ordinary public places, i.e., the people
in the crowd are not unified and purposeful. The direction of movement of the crowd is irregular when
there is no abnormality. This paper uses the anomaly detection algorithm with improved acceleration
characteristics to detect the abnormal escape behavior of the crowd. Firstly, the motion vector field is
processed by block processing, then the image is filtered to reduce the influence of noise, and the mean
filtering is adopted, and then the algorithm is used to extract the foreground of the image sequence. This
kind of operation not only facilitates the extraction of motion features, but also reduces the disadvantages
of large computational complexity. The experimental research shows that the algorithm has high accuracy
in identifying abnormal behavior and has high practical value, which can meet the accuracy and real-time
requirements of the security system.

2 RALATED WORK
In recent years, with the continuous deepening of the research on abnormal behavior detection of people,
related technologies have gradually matured. According to the degree of occurrence of abnormal events
in the surveillance video picture, they can be divided into global abnormalities and local abnormalities. A
global exception means that an abnormal behavior occurs in the entire monitored image (even if part of
the area is normal). A local anomaly is an abnormal behavior in which a local area in a surveillance video
is distinguished from a surrounding area. In addition, the focus of the research method is different for the
[1]
different characteristics of global anomalies and local anomalies . Scholars have proposed some more
classical methods to promote the continuous development of population anomaly detection technology.

For the global exception, it is necessary not only to detect whether the scene is abnormal, but also to judge
the start and end of the abnormality and the intermediate transition phase. In general, the global anomaly
detection method is to analyze the change of the event based on the motion estimation of the entire video
picture.

[2]
Chen and Huang proposed a graph analysis algorithm based on eigenvalues. In their paper, each isolated
area in the video picture is regarded as a vertex, and the whole group is regarded as a picture. Moreover,
topological changes are simulated by local features (based on feature subgraph analysis and trigonometric
transformation) and global features (time, etc.). Finally, the simulated topological variation characteristics
are used to analyze the presence or absence of abnormal events in the population.