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Page 32 Xu et al. J Surveill Secur Saf 2020;1:16-33 I http://dx.doi.org/10.20517/jsss.2020.04

Therefore, when using traditional algorithms, it is easy to have false positives or false negatives. The
algorithm uses the actual distance threshold and judges the distance between the actual trajectory points of
the two pedestrians, which is not affected by any factors, thus the accuracy is higher.

It can be concluded from the results in Figure 10 that the accuracy of the proposed algorithm is
significantly improved compared with the traditional algorithm. The traditional algorithm calculates the
movement speed and movement angle of pedestrians based on the pixel coordinate trajectory. However,
since the camera in the security scene is generally fixed on a vertical wall rather than directly above
the surveillance scene, the angles of the pixels in the video tend to be different from the true angles. In
addition, the threshold in the traditional algorithm is set by the number of pixels according to experience,
which has no practical significance, thus the accuracy of the traditional algorithm is not high. In summary,
the algorithm has high accuracy in identifying abnormal behaviors and has high practical value, which can
meet the accuracy requirements of security systems.

Although the research presented in this paper has achieved good detection results in simulation
experiments on actual datasets, there are still some problems in the whole algorithm that need to be
improved. Firstly, the types of anomalies that can be identified in this article are not single, and they are
not capable of identifying complex and diverse situations. Secondly, the algorithm is suitable only for the
detection of abnormal behavior of low- and medium-density crowds, because when there are big crowds in
the scene, severe occlusions will make the results of the number estimation algorithm inaccurate. Finally,
the algorithm that detects the escape from the center does not detect more than three positions where
anomalies may occur, and the algorithm is executed after detecting the occurrence of crowd abnormal
events. How to optimize the calculation to achieve automatic and intelligent identification of the positions
of crowd abnormalities that may occur is a key issue to be studied in the future.

Based on data mining, this study combined cloud computing image processing technology to identify real-
time crime behavior. 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. At the same time, this study briefly illustrates that acceleration
is an important motion feature of crowd anomaly detection. It also shows that the acceleration-based
crowd anomaly detection algorithm is more reasonable than the traditional speed-based ones. Therefore,
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,
and then the image is filtered to reduce the influence of noise. Next, 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.

DECLARATIONS
Authors’ contributions
Made substantial contributions to conception and design of the study and performed data analysis and
interpretation: Xu Z
Performed data acquisition, as well as provided administrative, technical, and material support: Cheng C,
Sugumaran V

Availability of data and materials
Not applicable.