## 2 Related Work  
Video anomaly detection is critical in the domain of surveillance systems, as it addresses the need to identify anomalous segments within video streams. Over time, two major streams of methodologies have emerged for this critical task: handcrafted approaches and deep-learning-based methods. The former employs manual feature engineering techniques such as STIP, SIFT-3D, and optical flow histograms, whereas the latter makes use of the power of deep neural networks such as VGG and ResNet to process spatiotemporal data efficiently. The introduction of two-stream Convolutional Neural Networks (CNNs) for improved activity recognition and novel approaches to modeling long-term temporal dependencies are notable advancements. The literature includes a wide range of deep learning models, from ConvLSTM to attention-based architectures, all of which contribute to the improvement of anomaly detection in videos. Furthermore, weakly supervised techniques, generative models, and recent efforts to address anomalies in low-resolution videos have significantly expanded the scope of this evolving field. In the midst of these advances, our research focuses on a novel problem: detecting anomalies in multi-class scenarios in low-quality surveillance videos. We present a unified methodology that combines novel super-resolution techniques with a two-stream architecture, providing a comprehensive solution to the complexities of real-world surveillance scenarios. Manual feature engineering methods such as STIP, SIFT-3D, and optical flow histograms involve human intervention [26; 27]. While insightful, improved dense trajectory approaches like the one by [PERSON] et al. [28] surpass earlier handcrafted techniques. The advent of deep learning has revolutionized video anomaly identification, with networks like VGG and ResNet efficiently processing spatiotemporal data in videos [29; 30]. Noteworthy in this domain is the introduction of two-stream Convolutional Neural Networks (CNNs), which combine spatial and temporal inputs for improved activity recognition [31; 32]. Advancements in modeling long-term temporal dependencies have been achieved through techniques like temporal segment networks and 3D convolutional filters. [PERSON] et al. [33] introduced a temporal segment network that exhibited robust performance on benchmark datasets. The C3D method by [PERSON] et al. [34] addressed challenges in modeling temporal information and inspired subsequent work by [PERSON] et al. [35] for anomaly classification. Among deep-learning-based models, significant strides have been made, particularly in domains involving nonlinear, high-dimensional data. [PERSON] et al. [36] proposed a Convolutional Long Short-Term Memory (ConvLSTM) model for encoding video frames and identifying anomalies. [PERSON] et al. [37] introduced a Convolution-Block-Attention-based LSTM model that enhances spatial information accuracy. [PERSON] et al. [38] combined human posture estimation with a densely connected fully Convolutional Neural Network (CNN) for anomaly identification. [PERSON] et al. [1] utilized a recurrent neural net work (RNN) and a convolutional autoencoder for anomaly detection, while [PERSON] et al. [39] integrated temporal and spatial detectors for anomaly identification. Weakly supervised techniques, including C3D and MIL, have been employed for anomaly detection. [PERSON] et al. [2] combined weak video labels with Multiple Instance Learning (MIL). [PERSON] et al. [40] used a coordinate-based regression model for tube extraction. Generative models like GANs have been explored, with [PERSON] et al. [41] training GANs for visual anomaly detection. BatchNorm into Weakly Supervised Video Anomaly Detection (BN-WVAD) [42] has been used to capitalize on the statistical insight that temporal features of abnormal events often behave as outliers; BN-WVAD leverages the Divergence of Feature from Mean vector (DFM) function from BatchNorm. This DFM criterion serves as a robust abnormality indicator and identifies potential abnormal snippets in videos.