## 5 Experiments
### Case Studies with Visualizations
For scene 03, two test video anomaly scores, 0031 and 0032, are shown in Figure 10. The first shows a guy who hijacks the bag from his colleague and then starts chasing. In the latter, the anomalous event was represented by a person falling to the ground. As seen in Figure 10a, the model was able to correctly identify the complex abnormal situation with a high anomaly score, in the same manner as for the anomalous period in Figure 10b. In Figure 11, which exhibits the anomaly scores with shaded ground truth for videos 0144 and 0147 from the Shanghai scene 06, the model optimally recognized the unusually moving car on the sidewalk with extremely high scores, as shown in Figure 11a. On the other hand, Figure 11b depicts a cyclist with an umbrella and shows that the anomaly score was extremely high when this anomalous event occurred. As a result, the above figures show that the model performed exceptionally well in detecting distinct anomalous events and objects in the three different datasets. It demonstrated its efficacy in differentiating between normal and abnormal events in videos by producing high anomaly scores for anomalous intervals and low scores for normal ones. Figure 8: Anomaly score curves of two different videos from the CHUCK Avenue dataset: (**a**) Test 05 (anomaly type: throwing bag); (**b**) Test 06 (anomaly type: wrong direction and throwing bag). Figure 10: Anomaly score curves of two different videos for Scene 03 of Shanghai dataset: **(a)** Test03_0031 (anomal type: robbing and chasing); **(b)** Test03_0032 (anomaly type: falling). Figure 9: Anomaly score curves of two different videos for Scene 01 of Shanghai dataset: **(a)** Test01_0016 (anomaly type: motorcyclist); **(b)** Test01_0177 (anomal type: biker). The anomaly scores for four test videos with two scenarios from the CHAD dataset are displayed in Figures 12 and 13. The figures depict that the proposed model was able to identify different abnormal events with high score values. The abnormal event of \"biking\" from the Test 1_066_1 video of the CHAD-Cam 1 scenario is represented in Figure 12a, which shows that the abnormal event was successfully recognized. The \"falling\" and \"running\" events from the Test 1_084_1 movie of the same situation, as seen in Figure 12b, were also recognized with strong anomaly scores in the same way. For Test 2_077_1 from CHAD-Cam 2, two people appeared to fight, \"one is strangling the other\", then they chased, and the model was able to detect the whole complicated event, as shown in Figure 13a. A runner who appeared in Figure 13b, from the Test 2_095_1 video, was identified correctly with high anomaly scores, even though it appears as a small object, far from the camera position, and intertwined with objects in the scene background. Figure 11: Anomaly score curves of two different videos for Scene 06 of Shanghai dataset: (**a**) Test06_0144 (car in pedestrian walkway); (**b**) Test06_0147 (biker with the umbrella in pedestrian walkway). Figure 12: _Cont._  
On the other hand, it was found that some ShanghaiTech test videos contain badly annotated frames (minor falsely annotated frames), such as frames No. 151, 157, and 162 in video 06_0144, frame No. 184 in video 06_0147, and frame No. 72 in video 06_0150. In addition to frame No. 161 in video 03_0033 and frame No. 202 in video 02_0164, falsely annotated frames were excluded from the test. Accordingly, these videos need to be revised by the dataset authors. Figure 12: Anomaly score curves of two different videos for Cam 1 of CHAD dataset: (**a**) Test 1_066_1 (anomaly type: biker); (**b**) Test 1_084_1 (anomaly type: falling). Figure 13: Anomaly score curves of two different videos for Cam 2 of CHAD dataset: (**a**) Test 2_077_1 (anomaly type: strangling and chasing); (**b**) Test 2_095_1 (anomaly type: running).