# Real-Time Surveillance System for Analyzing Abnormal Behavior of Pedestrians
## 5 Conclusions  
In this paper, we proposed a real-time abnormal behavior analysis method of pedestrians on the road based on the detection and tracking of pedestrians. The proposed system determines intrusion and loitering based on detection and tracking, and transmits the cropped image of pedestrians to the abnormal behavior analysis module for abnormal behavior analysis. Instead of detecting a pedestrian every frame, we detect the pedestrian's location at regular intervals, and tracking it for the rest of frames. The cropped images are transmitted to the analysis module of the abnormal behavior in real-time, and the fall-down and violence are analyzed through the behavior recognition algorithm. Various functions were designed and configured to facilitate information management and communication between the two modules, and the real-time performance and excellent accuracy of the proposed algorithm were confirmed from the experimental results.  
In experimental results, the behavior of intrusion, loitering, fall-down, and violence were classified based on the KISA certification criteria, and performance evaluation was conducted in a real-time environment through VLC streaming. Classification performance of intrusion, loitering, and violence was very high. Although that of falls was slightly lower, it can be improved through additional algorithms such as pose estimation and additional dataset configuration. The proposed method is a real-time abnormal behavior analysis system, which is suitable for use in real environments. Intrusion, loitering, violence, and  
Figure 13: Fall-down/Violence Experimental Results: (**a**) Fall-down Experimental Results, (**b**) Violence Experimental Results.  
fall-down can be used in the surveillance system because they can be used as important indicators to determine crime prevention or emergency situations. In addition, what this paper proposes is a system for analyzing abnormal behavior in CCTV. If the behavior recognition dataset such as sports, arson, and abandonment is additionally trained according to the intended purpose, and an algorithm suitable for it is used, even the mentioned behavior can be analyzed.  
Conceptualization, D.K. and J.P.; methodology, soft ware, D.K. and H.K.; validation, formal analysis, D.K., Y.M. and H.K.; writing--original draft preparation, D.K. and H.K.; writing--weview and editing, H.K., Y.M. and J.P.; supervision, project administration, J.P. All authors have read and agreed to the published version of the manuscript.  
This work was partly supported by Institute for Information & communications Technology Planning & Evaluation(IITP) grant funded by the Korea government(MSIT)(No.2021-0-01341), by the National R&D Program through the National Research Foundation of Korea(NRF) funded by Ministry of Science and ICT(2020M3F6A1110350) and by the Institute of Civil-Military Technology Cooperation Program funded by the Defense Acquisition Program Administration and Ministry of Trade, Industry and Energy of Korean government under grant No. 19 CM5119.  
Not applicable.  
The KISA datasets presented in this study are openly available in [4].  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish.