# Real-Time Surveillance System for Analyzing Abnormal Behavior of Pedestrians
## 4 Experimental Results  
We proposed an efficient object management system to analyze abnormal behaviors. Figure 10 shows the framework of the proposed system. The proposed system takes image data as input and performs object detection and tracking. Tracked objects are managed through the object manager for correct behavior recognition. Objects managed in the ObjectManager are transmitted to the abnormal behavior recognition module after determining whether they are the same object to analyze the abnormal behavior. In this context, intrusion and loitering are detected in the object detection/tracking module, and fall-down and violence are detected in the action recognition module. We perform a performance evaluation of the system proposed in this section. The proposed system uses 3D-Resnet to recognize fall-down and violence behaviors. Table 2 shows existing action recognition algorithms, most of which use C3D and I3D feature extraction methods. Performance evaluation of action recognition in Table 2 is based on the UCF-Crime dataset. Since the action recognition algorithm can be used in various environments, it cannot be judged based on the performance in a specific dataset. For a specific system, it should be optimized through heuristic evaluation using several algorithms. Table 3 shows the results obtained by using KTH, UCF11, and KISA Datasets. Figure 10: Framework of the Proposed System. Figure 9: Violence/Fall-down Experimental Results: (**a**) Normal, (**b**) Fall-down behavior, (**c**) Fall-down. Recently, Convolutional 3D (C3D), I3D, and Residual 3D (R3D) methods are mainly used for action recognition algorithms. In Table 3, I3D and C3D showed high performance in the public dataset. However, if we compare the results of C3D, I3D, and Res3D in the dataset we built ourselves in the CCTV environment and the KISA dataset, I3D shows poor results. However, when comparing the results of C3D, I3D, and Res3D in the dataset we built ourselves in the CCTV environment and the KISA dataset, I3D shows poor results. On the other hand, the 3D-Resnet method shows good results. As the method used for comparison, the video was edited based on the pedestrian box, 265 clips were trained, and 97 clips were used as test data. The CCTV dataset including KISA used in the experiment consists of actions with short action times. Therefore, it is expected that there is a limit to the performance of I3D, which requires a lot of frame information. In this context, it can be seen that the performance of the action recognition algorithms is different depending on the behavior to be judged. As the 3D-resnet method requires a small number of frames, it shows excellent results in datasets such as fall-down and violence. Therefore, as the proposed action recognition algorithm, the 3D-Resnet method suitable for detecting the fall-down and violence is used, and the performance evaluation is performed using this 3D-Resnet based action recognition algorithm. The experiment was conducted using KISA Dataset, which is constructed for the purpose of analyzing abnormal behavior in a CCTV environment, and consists of a total of four scenarios: intrusion, loitering, fall-down, and violence. There are various behavior analysis methods using a specific action recognition dataset. However, in this paper, we propose a system for abnormal behavior analysis rather than an action recognition algorithm. Therefore, the behavior recognition algorithm used in this paper can be replaced by other algorithms, and in this section, comparison with other studies of the behavior recognition algorithm is meaningless. In this context, we evaluated performance using the KISA dataset, which can best represent the proposed contribution. Unlike the existing action recognition dataset, which consists of scenarios by distance, illumination, and situation. For that reason, the KISA dataset is the most appropriate for the proposed work. Table 4 shows the composition of the KISA dataset used in the experiment.