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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 25
Figure 7. Schematic block diagram
research object of this algorithm. The algorithm is mainly used to locate the escape center of the crowd,
and the escape center is defined as the position where the intersection of the motion vector is the densest.
Therefore, the removal of wild intersections should remove a smaller number of intersections in the search
window. The graphical method used in this paper uses many experiments to obtain empirical thresholds to
measure the denseness of intersections to assist in removing wild intersections [Figure 7].
4 EXPERIMENTAL EVALUATION
Step 1: Select the video. In the experiments, 10 segments of video were selected to test three kinds of
abnormal behaviors. The test video mainly came from a bank monitoring video, and some videos were
from the crime scene monitoring. Moreover, the surveillance video was recorded by Haikang or Dahua
cameras. Some test videos were taken by digital cameras, and these video formats were different, including
AVl, MP4, DAV, etc. In addition, the system unified the video sources to AVI format for easy processing.
The number of pedestrians appearing in the selected video, the time of appearance of the pedestrian, the
video resolution, etc. were all random and there was no law. In addition, because shadow removal is not the
focus of this paper, to reduce the impact of shadow on the algorithm, only video with less obvious shadow
was selected when selecting video.
Step 2: Parameter configuration. First, the camera corresponding to the selected video was calibrated, and
after the coordinate conversion was completed, the actual coordinate matrix was saved as an XML file (the
file name is the camera name, and the file suffix is .xml). Then, the estimated target imaging size, frame
interval, monitoring area, abnormal behavior type, etc. were saved in the PAR file (the file has the same
name as the test video file and the file suffix is .par).
Step 3: The result of manual statistics. The video was observed by the human eye, and the manual statistical
result was recorded in the MTR file (the file has the same name as the test video file, and the file suffix is
.mtr). Manual statistics were needed to record the type of abnormal behavior, start time, and end time.
Finally, the number of samples with abnormal behavior (abnormality) and no abnormal behavior (normal)
in the selected video were counted according to the behavior type. The statistical results are shown in
Tables 1-3.