## 4 Experimentation
### Experiment Results and Ablation Study
#### 4.4.2 Qualitative Results and Demonstration of Context-Dependency Learning of DMRMs  
The qualitative results of our proposed framework are shown in Figures 4 and 5, after testing our framework on videos from the testing datasets of UCF-Crime and ShanghaiTech. Figure 4 demonstrates how well our network learned the anomalous event detection task because of our framework's context-dependency learning. However, it failed in some cases like (\(\mathrm{e}\)), (\(\mathrm{f}\)) and (\(\mathrm{g}\)), where (\(\mathrm{e}\)) demonstrates the failure case in which DMRMs was unsuccessful due to extreme variation in lighting conditions, though success is achieved in (\(\mathrm{d}\)) in the presence of less lighting variation. Figure 4f demonstrates the case where DMRMs failed to differentiate between normal \"car washing\" and \"vehicle theft\" cases, while (\(\mathrm{g}\)) shows continuous anomalous event detection along with a low abnormality score because of random movements. On the other hand, (\(\mathrm{a}\)), (\(\mathrm{b}\)), (\(\mathrm{c}\)), and (\(\mathrm{d}\)) demonstrate the accurate detection of different anomalous events with different backgrounds and lighting conditions along with the location of moving objects. These show effectiveness of the proposed framework, DMRMs: (\(\mathrm{a}\)), (\(\mathrm{b}\)), (\(\mathrm{c}\)) and (\(\mathrm{d}\)) depict the accurate detection of a \"road accident,\" \"arson,\" \"cycling in a pedestrian walkway,\" and a \"normal event,\" respectively. Figure 5 demonstrates how well our network learned the anomalous event detection task when there are multiple anomalous events in a single video of proposed DMRMs (effect of proposed MRMs). However, it failed in some cases like (\(\mathrm{d}\)), which demonstrates the failure case in which a \"shooting\" is detected only a single time, although it is present two times in the same video, a mistake occurring due to occlusion by trees, which had some extent of information. Besides, (\(\mathrm{a}\)), (\(\mathrm{b}\)), and (\(\mathrm{c}\)) demonstrate the accurate detection of multiple instances of anomalous events in a single video with different backgrounds and lighting conditions (illumination variations) along with the location of moving objects. These illustrate the effectiveness of DMRMs (especially proposed MRMs) in the presence of multiple instances of anomalous events: (\(\mathrm{a}\)), (\(\mathrm{b}\)) and (\(\mathrm{c}\)) depict the detection of \"shoplifting,\" \"stealing,\" and \"driving a car in a pedestrian walkway,\" respectively. These multiple anomalous events were detected with a significant abnormality score in a single video. Figure 4: Qualitative analysis of the proposed framework, DMRMs, on UCF-Crime and ShanghaiTech testing datasets. 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. (\(\mathrm{a}\),\(\mathrm{b}\),\(\mathrm{d}\)–\(\mathrm{f}\)) are testing results from UCF-Crime dataset and (\(\mathrm{c}\),\(\mathrm{g}\)) are from ShanghaiTech dataset, where (\(\mathrm{e}\)–\(\mathrm{g}\)) are failure cases of our proposed framework, DMRMs. Meanwhile, all successful results of anomalous event recognition are shown in (\(\mathrm{a}\),\(\mathrm{b}\),\(\mathrm{d}\),\(\mathrm{e}\)). **Demonstration of context-dependency learning** appears in Figures 6 and 7. Figure 6 demonstrates the qualitative comparison between the framework without joint learning and our framework (DMRMs) with joint learning. The upper-left image in (a) reflects the result of an anomalous event without any joint learning of motion and appearance features that failed to recognize \"shoplifting\"--an anomalous event.