## 5 Experiments
### Case Studies with Visualizations
It is worth noting that the introduced approach does not perform optimally on all three datasets for the following reasons: the three datasets differ significantly in terms of shooting situations, colors, and perspectives, making it challenging for a system to produce comparable results across all datasets. Moreover, ViTs do not have CNNs' inherent inductive biases, and they frequently need a massive amount of data to be trained efficiently. However, they can outperform CNNs when they are trained, particularly on large-scale datasets. The proposed ViT + STRA-attention model proved this point when mixed combinations of the datasets were used as the detection accuracy improved despite the diverse natures of the three datasets, which include various illumination and environmental conditions, various view angles, different scenes with different quality and resolutions, and the large number and diversity of anomalous events and objects in the datasets. Moreover, CNNs use convolutional layers, assuming the spatial hierarchies and localizations in images. This strong inductive bias allows CNNs to learn well from very limited datasets by using the intrinsic structure of visual data. In contrast, ViTs use self-attention processes to identify associations between patches in pictures by treating them as a succession of patches. Because it makes no assumptions about the intrinsic order or locality among the patches, this method has less inductive bias concerning the spatial structure of images. This gives ViTs great flexibility and not only enables them to extract intricate relationships, but also implies that for them to learn these patterns efficiently, more data are needed. This reduced inductive bias might cause underfitting for the smaller training datasets, whereby the ViT might not pick up enough information about the relevant characteristics of the pictures to operate effectively on unobserved data. In addition, we believe that the proposed model is more sophisticated and thus able to perform on small datasets such as the UCSD-Ped2, since it consists of low-quality grey frames, however, it achieved promising results of 95.6%. On the other hand, a better performance was obtained with the much larger Avenue dataset, making it superior to the Shanghai dataset which is considered the largest dataset available for VAD. Additionally, VIT + STR attention enhances the detection of larger datasets (i.e., Shanghai and Avenue) compared to the baseline model for the different combinations of the datasets, as shown in Figure 7:0 We call the combined versions'very large datasets'. In addition, it outperforms the SOTA techniques for the Cam 1 and Cam 2 data from the largest CHAD dataset with AUC scores of 71.8% and 64.2%, respectively. This shows the ability of the proposed model to extract robust, effective, and complex features and successfully identify anomalies with high scores from extremely diverse videos captured from different environments, with different qualities, and in different conditions that contain a wide range of various anomaly events and types. However, we think that by taking into account the following issues and constraints, our framework might be improved in the future:  
1. We could benefit from the recent memory-augmented neural networks (MANNs) to improve the suggested approach. MANNs are networks that preserve a larger collection of representations throughout time by selectively storing and updating only the relevant information. Hence, these networks may be augmented in our model to improve its capacity to recognize more subtle and complicated patterns. Memory networks have efficient scaling abilities and adaptability to various scenarios and circumstances. Further, this could lead to high-accuracy anomaly detection for larger datasets, which is our focus in this research, and enhance anomaly detection in videos for real-time systems. As in real-world applications, the characteristics of anomalies could vary over time; hence, scaling capacity and adaptability are essential.