## 3 Analysis of AI Techniques in SVAD
### Algorithms
#### 3.2.5 Other Algorithms  
Two clustering methods are available. Their argument is based on the idea that normal data are clustered, whereas anomalous data are not [77] connected to any cluster. The second type is predicated on the idea that, whereas anomalies belong to tiny clusters, typical data instances belong to massive or dense clusters. Fuzzy traffic density and flow are built using fuzzy theory to identify abnormalities in complicated traffic videos [78]. Heuristic  
\begin{table}
\begin{tabular}{p{128.0 pt} p{284.5 pt}} \hline \hline
**Methods** & **Summary** \\ \hline FFP [57] & Spatial and motion constraints are used to estimate the future frame for normal events in addition to density and gradient losses. **Datasets:** UCSD, ShanghaiTech, Avenue; **Techniques:** GAN, optical flow. **A model combining CNN and bidirectional LSTM is proposed to recognize human movement in video sequences. **Datasets:** YouTube 11 Actions, UCF-101, HMDB51; **Techniques:** LSTM, CNN. **By using the effective gradient and quadratic-programming-based training methods, the parameters of the LSTM architecture and the support vector data description algorithm are trained and optimized. **Datasets:** Avenue, Subway, ShanghaiTech, UCSD; **Techniques:** LSTM, one-class SVM. **A Self-Supervised Predictive Convolutional Attentive Block (SSPCAB) is proposed, which can be easily incorporated into various anomaly detection methods. The block acquires the ability to recreate the masked area utilizing contextual information for each site where the dilated convolutional filter is applied. **Datasets:** Avenue, MVTec AD, ShanghaiTech; **Techniques:** CNN, convolutional attentive block. **A neural network built with transaction blocks, including dictionary learning, feature learning, and sparse representation, is proposed. A novel long short-term memory was also proposed and reformulated using an adaptive iterative hard-thresholding technique (LSTM). **Datasets:** UCSD, Avenue, UMN; **Techniques:** LSTM, RNN-based sparsity learning. **An Incremental Spatiotemporal Learner (ISTL) model is proposed to address the difficulties and limitations of anomaly detection and localization to keep track of the changing character of anomalies through active learning using fuzzy aggregation. **Datasets:** UCSD, Avenue; **Techniques:** convolutional LSTM, fuzzy aggregation. **Using a light-weight CNN and an attention-based LSTM for anomaly detection reduces the time complexity with competitive accuracy. **Datasets:** Avenue, UCF-Crime, UMN; **Techniques:** residual attention-based LSTM. **A Conv-transformer is used to perform future frame prediction. Dual-discriminator adversarial training maintains local consistency and global coherence for future frame prediction. **Datasets:** UCSD Ped2, Avenue, ShanghaiTech; **Techniques:** GANs; transformer; CNN. **Using a ViT model for anomaly detection involves processing a single frame as one patch. This approach yields good performance on the SVAD task while maintaining the advantages of the transformer architecture. **Datasets:** UCSD Ped2, Avenue, ShanghaiTech; **Techniques:** vision transformer. \\ \hline \hline \end{tabular}
\end{table}
Table 5: Prediction-based methods.  
techniques make decisions regarding anomalies based on feature values, geographical locations, and contextual data intuitively [79]. However, many real-world systems do not rely only on one technology. Using a light-weight CNN and an attention-based LSTM for anomaly detection reduces the time complexity with competitive accuracy.