## 3 Analysis of AI Techniques in SVAD
### Algorithms
#### 3.2.3 Reconstruction-Based Algorithms
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Methods** & **Summary** \\ \hline One-class classification [51] & A histogram of optical flow orientation is integrated with a one-class SVM to identify abnormal events. Modeling high-density scenes may be performed quickly and precisely using optical flow techniques. Pattern identification is performed after feature extraction to discriminate between regular and irregular activities. **Datasets:** UMN; **Techniques:** SVM, optical flow, histogram of optical flow orientation.  The crowd escape anomaly is detected using statistical and deep learning algorithms that directly evaluate the pixel coordinates. **Datasets:** UCSD, Avenue, ShanghaiTech; **Techniques:** YOLOv3, GAN-based frame predictor, kNN. To detect abnormalities from video surveillance while precisely estimating the start and end times of the anomalous event, a decision-tree-enabled solution leveraging deep learning was created. **Datasets:** ImageNet, COCO; **Techniques:** decision trees, YOLOv5.  A new approach combines an AE-based method with single-class deep feature classification. An AE is trained using normal images; then, anomaly maps are embedded using a pre-trained CNN feature extractor. A one-class classifier with kNN is trained to calculate the anomaly score. **Datasets:** MVTec; **Techniques:** convolutional autoencoder, high-density embedding, one-class classification. There is a high probability of overfitting as abnormal datasets are insufficient. The Interpolated Gaussian Descriptor (IGD) method, an OCC model that learns a one-class Gaussian anomaly classifier trained with inversely interpolated training samples, is proposed to solve this problem. The IGD is used to learn more meaningful data descriptions from typical normal samples. The crowd escape anomaly is detected using statistical and deep learning algorithms that directly evaluate the pixel coordinates. **Datasets:** MNIST, Fashion MNIST, CIFAR10, MVTec AD; **Techniques:** Gaussian classifier. A classifier that is simultaneously trained to give the GAN samples less confidence is used in conjunction with a GAN. Samples from each test distribution of anomalies are used to arrange the classifier and GAN. **Datasets:** CIFAR, tree-enabled, LSUN; **Techniques:** DNN, GAN, Kullback-Leibler, Gaussian distribution. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification-based methods. generator and examples from the target domain. Through this process, the generator network learns to create examples indistinguishable from examples in the target domain. In summary, reconstruction-based methods such as AEs and GANs have shown promising results in anomaly detection tasks by mapping normal data into a lower-dimensional domain and identifying anomalies based on the reconstruction error. Variants of AEs such as Conv AEs and variational AEs have also been utilized in this domain. These methods are part of a larger field of adversarial generative methods that include generative adversarial networks. Some reconstruction-based studies are shown in Table 4.