## 2 Related Works  
Computer vision related to the monitoring and detection of anomalous behavior has been studied under different conditions and environments for different purposes using different methods. Some of the studies are addressed below. The authors of [7] present a system that analyzes human motion from the position and movement of objects within three-dimensional spaces in in-depth images. The system uses a depth sensor and a computer vision algorithm to detect human poses and movements and subsequently executes a machine learning model to recognize specific behaviors. In [12], computer vision monitors worker productivity and safety for the purpose of minimizing manual monitoring in industries using an object recognition algorithm (YOLOv3) trained using images. Likewise, ref. [13] presents a vision-based fault classification approach by utilizing cameras to capture real-time images from robot movements and components to monitor industrial robots. The aim is to ensure worker productivity and product quality. In contrast, ref. [14] focuses on designing and implementing a system that uses a combination of shape- and motion-based features to recognize human actions based on silhouettes. The system consists of preprocessing, silhouette extraction, feature extraction, and classification modules. A support vector machine classifier is implemented to classify human behavior. Furthermore, in [15, 16], image analysis is performed on images where objects and body poses remain stable. Subsequently, ref. [17] proposes a multi-view learning approach to detect anomalous human behavior in complex and dynamic environments using multiple data sources, such as sensor data, video feeds, and contextual information. In addition, the authors of [18] conduct video monitoring using intelligent behavior identification techniques to detect suspicious behavior and alert security personnel. The system utilizes a camera to capture footage that is subsequently processed to identify suspicious worker behaviors based on human body movement analysis. As a result, the proposed system reduces the number of false alarms and enables security personnel to promptly respond to potential threats. Apart from this, ref. [19] focuses on safety monitoring at construction sites to prevent accidents and injuries using an anomaly detection algorithm developed in a random finite set framework. Later, ref. [20] presents an unsupervised anomaly-based approach for pedestrian age classification in surveillance camera footage, and the proposed framework incorporates an adversarial model with skip connections. The results indicate the potential of this approach to classify pedestrian age and detect anomalous age patterns. In addition, in [21], an online and adaptive method is utilized to detect abnormal events in video surveillance using a spatiotemporal ConvNet. The method combines spatial and temporal information and incorporates an adaptive learning approach to dynamically update the model, thus improving the detection accuracy using normal events as training samples. A different study [22] highlights the importance of anomaly detection in the elderly daily behavior domain. The study presents diverse methods and approaches as valuable monitoring tools to detect possible anomalous situations that potentially indicate warning signs for chronic illnesses or initial physical and cognitive decline. The study is specifically designed for caregivers, healthcare professionals, and family members to enhance their overall safety and quality of daily life. Ultimately, the integration of advanced technologies and machine learning in eldercare conceivably strengthens the well-being and independence of the elderly population, while facilitating and supporting caregivers in delivering professional duties. Moreover, ref. [23] presents a performance analysis of convolutional neural networks (CNN) using a long short-term memory (LSTM) network to effectively capture and analyze spatiotemporal information from surveillance video data for surveillance systems.