# A Review of Abnormal Crowd Behavior Recognition Technology Based on Computer Vision
## 4 Research Status of Abnormal Crowd Behavior Recognition  
In recent years, traditional learning methods have accumulated rich research results in the field of abnormal crowd behavior recognition. However, with the increasing complexity of crowd scenes, the recognition performance of traditional methods has certain limitations and it is often difficult to capture the subtle differences and dynamic changes in abnormal  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Region Type** & **Crowd Sample** & **Sensitivity Level** & **Recommended IPC Resolution** & **Lens Requirement** & **Frame Rate (fps)** & **Recommended GPU** & **Remark** \\ \hline Train station waiting room & 500-1000 & High & 3840 \(\times\) 2160 & Wide-angle to standard focal length & 25–30 & \begin{tabular}{c} Gigabyte GeForce \\ RTX3060/From \\ China, Be \\ purchased from \\ JD.com) \\ \end{tabular} & High human flow & \begin{tabular}{c} High human flow \\ density, requiring a \\ high level of \\ sensitivity to ensure \\ safety \\ \end{tabular} \\ Subway platform & 100–200 & Middle & 2560 \(\times\) 1440 & \begin{tabular}{c} Standard focal \\ length \\ \end{tabular} & 25–30 & \begin{tabular}{c} Gigabyte GeForce \\ RTX3060/From \\ China, Be \\ Gegabyte GeForce \\ RTX3060/From \\ China, Be \\ \end{tabular} & High human flow & \begin{tabular}{c} Ineding to balance \\ false positives and \\ false negatives \\ false negatives \\ \end{tabular} \\ Popular shopping plaza & 800-1200 & High & 3840 \(\times\) 2160 & Wide-angle focal length & 20–25 & \begin{tabular}{c} China, Be \\ purchased from \\ D.com) \\ \end{tabular} & \begin{tabular}{c} Large open space, \\ allowing a certain \\ range of errors \\ \end{tabular} \\ Entrance of sports venue & 200–500 & Middle & \begin{tabular}{c} Standard to \\ wide-angle \\ 1920 \(\times\) 1080 \\ \end{tabular} & \
\begin{tabular}{c} Gigabyte GeForce \\ RTX2070/From \\ Israel might \\ \end{tabular} & 25–30 &
\begin{tabular}{c} During special \\ events, there may be \\ an extremely high \\ density of people \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: The recommended parameters for hardware resources.  
behaviors. Against this background, the development of deep learning technology and the method of integrating neural networks for efficient pedestrian abnormal behavior recognition have gradually become research hotspots. Depending on whether neural network elements are included in the model construction, the existing abnormal behavior recognition technologies can be divided into two main classifications [19]: traditional methods-based and deep learning-based methods. The recognition technologies based on traditional methods can be classified into 4 categories: methods based on statistical models [20], based on motion features [21], based on dynamic models [22], and based on clustering discrimination [23]. And the deep learning-based methods roughly include 5 categories: methods based on Convolutional Neural Networks (CNNs) [24], based on autoencoders (AE) [25], based on generative adversarial networks (GAN) [26], based on Long Short-Term Memory Networks (LSTM) [27] and based on the self-attention mechanism (Self-Attention) [28].