## 4 Experimentation
### Experiment Results and Ablation Study
#### 4.4.3 Ablation Study  
First, we demonstrate the results of dividing the videos into different numbers of segments, 8, 16, 32, 48, and 64--an essential step of our framework (Figure 1)-by studying their effect of the frame-level AUC metric on the UCF-Crime and ShanghaiTech datasets (given in Table 3). We concluded that the effect of the number of segments was more significant on the ShanghaiTech dataset than on the UCF-Crime dataset, and that dividing the videos into 16 segments provided an optimal performance on both datasets. The pre-trained 3D ResNet-34 [21] extracts features from each video clip of 16-frames. Therefore, in  
Figure 6: Qualitative comparison between the framework without joint learning and our proposed framework, DMRMs, with joint learning (or context-dependency learning) on UCF-Crime testing dataset. The red curve shows the abnormality score of the corresponding frame (ranging between 0 and 1). The light blue box shows the ground truth of the temporal region of anomalous events, while the arrow indicates the corresponding visual frame of the recognized anomalous event along with the localization of moving objects. The left-side images in (**a**,**b**) show the cases of the framework without joint learning, whereas the right-side images show the effect of joint learning in the same cases.  
Figure 7: Qualitative analysis of context-dependency learning of our proposed framework, DMRMs, on UCF-Crime testing dataset. The red curve shows the abnormality score of the corresponding frame (ranging between 0 and 1). The light blue box shows the ground truth of the temporal region of anomalous events, while the arrow indicates the corresponding visual frame of the recognized anomalous event. (**a**,**c**,**e**) depict cases of anomalous events, whereas (**b**,**d**,**f**-**h**) demonstrate normal events cases. Additionally, events (**a**,**c**,**e**) have background similarities with the events (**b**,**d**,**f**,**h**), respectively. Our proposed framework successfully recognizes all the events from (**a**–**h**) though having background similarities, demonstrating the context-dependency learning of DMRMs.  
this work, a lower limit of the number of segments is considered 16, although the effect of 8 segments on AUC of ShanghaiTech testing dataset is shown in Table 3. Moreover, dividing the video into 16 segments speeds up our framework (as shown in Table 7), DMRMs. Therefore, we chose \(N=16\) in our study for both datasets.  
Second, we demonstrate in Tables 4 and 5 the effectiveness of using our proposed MRMs along with 3D ResNet-34 and motion flow maps, a framework that outperforms the state-of-the-art methods on the UCF-Crime and ShanghaiTech datasets and mitigates the FAR as well. In Table 4, we show the effect of each ranking measure of our framework, DMRMs, on the AUC of both datasets. From Table 4, we can conclude that introducing higher-order ranking measures degrade the performance of the anomalous event detector, while a combination of ranking measures \(rm1\), \(rm2\), and \(rm3\) increases the AUC of our detector. Table 5 shows the lowest FAR value on the UCF-Crime testing dataset, and these FAR values were calculated on the normal videos of the dataset at 50% of the threshold. The smallest FAR value demonstrates that our framework, DMRMs, mitigates false alarms.