# A Review of Abnormal Crowd Behavior Recognition Technology Based on Computer Vision
## 6 Conclusions  
This review conducts a comprehensive and in-depth study of abnormal crowd behavior recognition technology, performing detailed analyses from four dimensions: basic definitions, traditional methods, deep learning, and performance indicators. Through a  
\begin{table}
\begin{tabular}{l l l l l l l l l l l l} \hline \hline \multirow{2}{*}{**Classification**} & \multirow{2}{*}{**Method**} & \multicolumn{1}{c}{\multirow{2}{*}{**UCSDPed1**}} & \multicolumn{4}{c}{\multirow{2}{*}{**UCSDPed2**}} & \multicolumn{2}{c}{**UMN**} & \multicolumn{2}{c}{**Shanghai Tech**} & \multicolumn{2}{c}{**Avenue**} & \multicolumn{1}{c}{\multirow{2}{*}{**Year**}} \\ \cline{3-14}  & & & **EER** & **AUC** & **EER** & **AUC** & **EER** & **AUC** & **EER** & **AUC** & **EER** & **AUC** \\ \hline \multirow{2}{*}{\ \begin{tabular}{l} Motion Feature \\ Clustering \\ Discrimination \\ \end{tabular}  } & \multirow{2}{*}{
\begin{tabular}{l} HAVA + HOF [35] \\ SFA [37] \\ DBSCAN [41] \\ ECNN [52] \\ ASDL [53] \\ TSCNN [54] \\ DSTCNN [55] \\ LDAN-Net [56] \\ CAE-UNet [62] \\ PMAE [64  
clear classification of crowd levels, \"crowd density\" is quantified and divided into five levels. Next, this study analyzes recognition techniques for traditional abnormal behavior based on statistical models, motion features, dynamic models, and cluster discrimination. Although these methods are effective in specific scenarios, they still have limitations in dealing with high-dimensional, nonlinear, and complex dynamic data, such as reliance on manually designed features and limited generalization ability. With the development of AI technologies, deep learning has become a research hotspot because of its strong automatic feature learning ability and good adaptability to complex patterns. This article discusses the methods based on the Convolutional Neural Network (CNN), Generative Adversarial Network (GAN), Long Short-Term Memory Network (LSTM), Autoencoder (AE), and Self-Attention Mechanism (SA) in detail, demonstrating their outstanding performance in abnormal behavior recognition. These methods can extract high-level features from raw data and effectively distinguish between normal and abnormal behaviors, thereby improving recognition accuracy and robustness. In conclusion, by comparing the performance indicators of each algorithm model in Tables 4 and 6, it can be found that deep learning methods show significant advantages in dealing with complex and changeable scenarios. Although each model has its application scope and limitations, combining different methods can achieve better results. For example, algorithms such as S2-VAE, TS-CNN, and SA-CNN have achieved recognition accuracy of more than 99% in dealing with some datasets in Table 6. Therefore, in practical applications, choosing the appropriate single model or combining multiple methods based on specific requirements can effectively enhance the overall performance of the abnormal behavior recognition system and achieve more accurate and reliable detection results. Future research can focus on improving the fusion ability of deep learning and multi-modalities, introducing context understanding and situational reasoning, and improving the robustness and adaptive learning ability of the model. It can help further broaden the recognition of multi-source and multi-dimensional data fusion, construct a self-consistent metaverse virtual-real fusion evolution model, and establish a \"perception-prediction-intervention-construction\" virtual-real fusion presentation mechanism. The continuous innovations in this field are expected to achieve a transformation from passive monitoring to active prevention in the future and focus on more intelligent, precise, and automated abnormal behavior recognition systems. Conceptualization, R.Z. and C.L. ; methodology, R.Z. and F.H. ; software, F.H. ; validation, formal analysis, and investigation, R.Z., F.H. and B.W. ; resources, data curation, writing--original draft preparation, writing--review and editing, and visualization, R.Z., F.H., C.L. and B.W.