## 3 Analysis of AI Techniques in SVAD
### Algorithms
#### 3.2.4 Prediction-Based Algorithms  
Prediction-based techniques can identify anomalies by assessing the difference between the expected and actual spatiotemporal properties of a feature descriptor [57]. These models assume that normal activities are predictable, and any deviation from the prediction indicates an anomaly. They typically use a **Recurrent Neural Network (RNN)** to predict the next frame in the sequence, given the previous frames. The model minimizes the difference between the predicted frame and the ground truth during training. Here, are some commonly used algorithms:  
**Long Short-Term Memory (LSTM)** is the most-widely used neural array model, combining the principles of the forget gate, entry gate, and exit gate and successfully avoiding back-propagation errors caused by vanishing/exploding gradients. \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Methods** & **Summary** \\ \hline ST-AE [60] & The spatiotemporal AE comprises one encoder and two decoders of 3D convolutional layers. It employs parallel training of decoders with monochrome frames, which is noteworthy compared to the distillation process. \\  & **Datasets:** Traffic, UCSD, Avenue; **Techniques:** CNN, autoencoder. The appearance and motion DeepNet model employs AEs and a modified two-stream network with an additional third stream to improve detection performance. The two-stream method has two major drawbacks: the requirement for a pre-processing technique, such as optical flow, which may be costly for real-world applications, and multiple networks for inference. \\  & **Datasets:** Train, UCSD; **Techniques:** one-class SVM, optical flow. The Gaussian mixture fully convolutional-variational AE uses the conventional two-stream network technique and uses a variational AE to enhance its feature extraction capability. This method estimates the appearance and motion anomaly score before combining the two clues to provide the final detection results. \\  & **Datasets:** Avenue, UCSD; **Techniques:** convolutional autoencoder, Gaussian mixture model. This method uses the convolutional AE and long short-term memory to detect anomalies. The framework produces the error function and reconstructed dense optical flow maps. \\  & **Datasets:** Avenue, UCSD; **Techniques:** convolutional autoencoder, LSTM, optical flow. A conditional GAN is trained to learn two renderers that map pixel data to motion and vice versa. As a result, normal frames will have little reconstruction loss, while anomalous frames will have significant reconstruction loss. \\  & **Datasets:** Avenue, ShanghaiTech; **Techniques:** GAN, LSTM, optical flow. An attention-based autoencoder using contentious learning is proposed to detect video anomalies. \\  & **Datasets:** UCSD, Avenue, ShanghaiTech; **Techniques:** GAN, autoencoder. A 3D CAE-based competitor anomalous event detection method is proposed to obtain the maximum accuracy by simultaneously learning motion and appearance features. It was developed to explore spatiotemporal features that help detect anomalous events in video frames. \\  & **Datasets:** UCSD, Avenue, Subway, ShanghaiTech; **Techniques:** convolutional autoencoder. A two-stream model is created that learns the connection between common item appearances and their related motions. A single encoder is paired with a U-net decoder to predict motion and a deconvolution decoder that reconstructs the input frame under the control of the \(l_{p}\) reconstruction error loss terms using a single frame as the input. \\  & **Datasets:** UCSD, Avenue, Subway, Traffic; **Techniques:** convolutional autoencoder. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Reconstruction-based methods. The **convolutional LSTM** is an LSTM variation that addresses the precipitation nowcasting problem. In contrast to LSTM, convolution operations are employed to calculate the feature maps instead of matrix operations, resulting in a significant decrease in the count of the training parameters of the model [59].