## 3 Analysis of AI Techniques in SVAD
### Algorithms
#### 3.2.2 Classification-Based Algorithms  
One of the most-widely used methods for SVAD is classification-based methods, which involve training a classifier to distinguish between normal and anomalous video frames or segments. The first step in using classification-based methods for video anomaly detection is to extract features from the video frames. These features can include spatial and temporal information, such as color, texture, motion, and object shape. Several feature extraction techniques have been proposed in the literature, including hand-crafted features, such as the Histogram of Oriented Gradients (HOG) and Scale-Invariant Feature Transform (SIFT), as well as in-depth learning-based features, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). Once the features have been extracted, the next step is to train a classifier to distinguish between normal and anomalous video frames or segments. Several classifiers have been proposed in the literature, including traditional machine learning classifiers, such as Support Vector Machines (SVMs), random forests, k-Nearest Neighbors (kNNs), and deep learning-based classifiers: CNNs and RNNs. The choice of the classifier will depend on the specific application and the type of features that have been extracted. After the classifier has been trained, it can classify new video frames or segments as normal or anomalous. The classifier will output a score or probability for each frame or segment, indicating the likelihood that it is normal or anomalous. A threshold is usually set to make a final decision, and any frames or segments with a score below the threshold are considered anomalous. One of the main advantages of classification-based methods for video anomaly detection is that they can be fine-tuned to a specific application by selecting appropriate features and classifiers. However, one of the main challenges is that these methods require a large amount of labeled training data to be effective. Additionally, they may be unable to detect anomalous events significantly different from the training data [43]. Several classification algorithms have been proposed in the literature on data science, which can be considered the most common in the field, and they were discussed in detail in [50]. Some commonly used algorithms are summarized as follows. **Support Vector Machine (SVM)** is a widely used classification, regression, or other application method. An SVM generates a single hyperplane or a set of hyperplanes in a high or endless space. The goal is to separate the two classes using a hyperplane that reflects the greatest separation or margin. The larger the margin, the smaller the generalization error of the classifier is. **k-Nearest Neighbors (kNN)** is a non-parametric supervised learning technique, also referred to as a \"lazy learning\" method. It maintains all occurrences that match the training set in an n-dimensional space, rather than focusing on building a large internal model. kNN uses data and employs similarity metrics to categorize new data points. **Decision Tree (DT)** is a popular non-parametric SL approach. Both the classification and regression tasks are performed using DT learning techniques. The DT is a recursive operation; it starts with a single node and branches into a tree structure. Some classification-based studies are shown in Table 3.