# Masked Convolutions Within Skip Connections for Video Anomaly Detection
## 7 Conclusions  
In the evolving landscape of video anomaly detection, this research introduces a novel framework employing the innovative MaskedSkipUNet architecture. By strategically integrating MaskedConv3D layers within skip connections, we have enhanced the model's prowess in identifying anomalies, particularly in complex surveillance footage. Our methodology's robustness is evident from the empirical evaluations conducted on varied datasets, including prominent ones like Ped2, CUHK Avenue, and ShanghaiTech. Figure 9: Reconstruction outputs from ablation models on a Ped2 frame with an anomalous bicycle and normal pedestrians. Underneath the title of each model is the reconstructed frame (top) and its pixel-wise difference from the original (bottom), illustrating the impact of each architectural variant on anomaly suppression. Moreover, a preliminary exploration on a 2D image dataset establishes the versatility and adaptability of our approach as a precursor to its application on intricate video datasets. Our findings shed light on the fundamental importance of masked convolutions, especially in preserving and emphasizing spatial details, vital for distinguishing anomalies. The MaskedSkipUNet, while built upon existing frameworks like UNet, carves a distinctive niche for itself by excelling in anomaly detection tasks where traditional methods falter. Furthermore, our qualitative and quantitative results, underpinned by compelling visualizations, underscore the merits of our proposed architecture. The ability of MaskedSkipUNet to extrapolate normal features, even in the presence of anomalous regions, sets it apart, enabling more accurate anomaly predictions and reconstructions. However, our approach is not without limitations. One notable drawback arises when anomalies are large in size. Since the model relies on reconstructing masked regions using surrounding spatio-temporal context, it performs best when anomalies are small and the surrounding context is largely normal. In scenarios involving large-scale anomalies or scenes where much of the surrounding content is also abnormal, the model may lack sufficient normal reference to accurately infer a normal reconstruction. This constraint could reduce the model's effectiveness in highly dynamic environments or in scenes where the anomaly encompasses a substantial portion of the frame. Additionally, the datasets we consider do not include variations in weather conditions. To ensure robustness in outdoor deployments across different climates, appropriate preprocessing techniques would need to be applied before using our model, as discussed in [60; 61]. Looking forward, future research could explore augmenting the MaskedSkipUNet architecture with diffusion-based mechanisms. Specifically, the reconstructed output from the model could be treated as a slightly less noisy version of the original input and recursively passed back into the MaskedSkipUNet. Training the model to refine its reconstructions across multiple iterations would allow it to gradually suppress anomalies treated as noise and recover finer normal details. This iterative refinement process could significantly improve the model's sensitivity to subtle and low-contrast anomalies, making it better suited for real-world applications that demand high precision. In conclusion, the MaskedSkipUNet heralds a promising leap forward in the domain of video anomaly detection. The foundation laid by this research paves the way for subsequent investigations and enhancements, driving the future evolution of this domain. As anomaly detection continues to hold paramount importance across numerous applications, from surveillance to industrial quality control, the strides made in this work ensure that our methodologies remain at the forefront of this ever-evolving discipline. Conceptualization, D.L. ; methodology, D.L. ; software, D.L. ; validation, D.L., V.A. and D.M. ; formal analysis, D.L. ; investigation, D.L. ; resources, D.L. ; data curation, D.L. ; writing--original draft preparation, D.L.