## 1 Introduction  
In the era of 5G and IoT, video surveillance is a critical component of modern security and monitoring strategies. This surveillance relies on advanced camera technology to observe and analyze diverse environments and contributes to applications such as security, crime prevention, safety, emergency response, traffic monitoring, and behavior analysis [1, 2, 3]. Video surveillance contributes significantly to theft prevention, traffic management, and overall safety in the residential, commercial, and industrial sectors. The incorporation of technology and machine learning [4, 5] into video surveillance, particularly in 5G and IoT environments, initiates unprecedented possibilities. Automated video surveillance systems controlled by computer vision algorithms [6, 7, 8] detect anomalies, changes in motion, and intrusions in real-time, reducing reliance on human monitoring [9]. However, challenges persist, such as operator errors, false alarms, and limitations in contextual information within video footage [10, 11, 12]. In the context of 5G and IoT, this study addresses technical limitations associated with low-quality videos: specifically, poor lighting and low spatial resolution. These difficulties have an impact on the perceptual quality of video streams [13, 14, 15] and introduce factorssuch as poor lighting, camera noise, low spatial resolution, and low frame rates [9; 16; 17; 18; 19]. Despite these challenges, various techniques for detecting anomalies in low-quality surveillance videos have been proposed, [20; 21]. Two primary approaches are commonly used to address the challenge of detecting anomalies in low-quality videos. The first entails improving video quality with techniques like denoising, dehazing, and super-resolution [22; 23]. An alternative strategy is to use deep learning methods directly for anomaly detection in low-quality videos [24; 25]. This study outperforms existing approaches by introducing a new super-resolution technique called \"TempoFuseNet\". For enhanced anomaly detection, this innovative framework employs a two-stream architecture that combines spatial and temporal features. The spatial stream extracts features using a pre-trained Convolutional Neural Network (CNN), whereas the temporal stream captures short-term temporal characteristics efficiently using a novel Stacked Grayscale 3-channel Image (SG3l) approach. The extracted features from both streams are fused via a Gated Recurrent Unit (GRU) layer to leverage long-term temporal dependencies effectively. The contributions of this study include the identification of challenges related to intra-class and inter-class variabilities, the introduction of a super-resolution technique leveraging an encoder-bank-decoder configuration, the incorporation of a StyleGAN for feature enhancement, and the proposal of a two-stream architecture for anomaly classification. Recognizing the nuanced landscape of automated surveillance systems is essential in the continuum of addressing challenges in video surveillance. These systems play a critical role in overcoming the limitations of manual monitoring. Despite their potential, however, these systems face challenges that necessitate strategic interventions for further refinement. One significant challenge is the generation of false alarms, which can overwhelm security personnel and undermine the effectiveness of surveillance operations. False alarms not only divert attention but also place unnecessary demands on resources. The importance of minimizing false alarms as a fundamental aspect of optimizing automated surveillance systems is acknowledged in this study. Another problem stems from video's inherent limitation in providing comprehensive context. Surveillance videos frequently capture snippets of events, making it difficult to decipher the intentions of those being watched or comprehend the full scope of a given incident. Improving the contextual understanding of surveillance footage appears to be a critical component in addressing this challenge.