# A Review of Abnormal Crowd Behavior Recognition Technology Based on Computer Vision
## 5 Comparative Analysis of Different Algorithm Experiments
### Experimental Comparison  
Table 6 summarizes the performance of traditional methods and deep learning-based abnormal behavior recognition methods on UCSD, UMN, Shanghai Tech, and Avenue datasets in recent years. This paper compares and presents evaluation indicators such as AUC and EER. These evaluation data are all cited from research papers in recent years.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Name** & **Scene Description** & **Scale** & **Resolution** & **Abnormal** & **Object** & **Limitation** \\ \hline \multirow{8}{*}{UMN [89]} & Video clips of & & & & & \\  & pedestrian & & & & & \\  & activities in & & & & & \\  & different & 8010 frame & & & & \\  & backgrounds such & 11 Video segment & & & & \\  & as campuses, & & & & & \\  & shopping malls, & & & & & \\  & and streets & & & & & \\ \hline \multirow{4}{*}{Shanghai-Tech [90]} & 13 campus area & & & & & \\  & scenes with & 317,398 frame & & & & \\  & complex lighting & 130 Video & & & & \\  & conditions and & segment & & & & \\  & camera angles & & & & & \\ \hline \multirow{2}{*}{CUHK-Avenue [91]} & Video surveillance & 30,625 frame & & & & \\  & clips of outdoor & 16 Training & & & & \\  & public places & & segments & & & \\  & public places & 21 Test segments & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: _Cont._According to the comparison of the experimental results, the following conclusions can be drawn:  
(1) The limitations of recognition technologies. Although most methods have achieved high recognition rates on some datasets, on datasets such as UCSDPed1, the EER and AUC values of many methods indicate that there are certain false alarms or missed alarms, meaning that there is still room for further optimization of abnormal behavior recognition technology, especially when dealing with specific types of data or complex scenarios;  
(2) The advantages of temporal models. Two-dimensional convolutional networks that only rely on spatial features often perform worse in abnormal behavior recognition tasks than those models that integrate optical flow features, 3D convolution, and LSTM. These models incorporate temporal information and can more comprehensively capture the dynamic changes in video sequences, not only considering spatial features but also in-depth analysis of the continuity and change trends of behavior patterns in the time dimension;  
(3) The advantages of Autoencoder Network. The hidden feature representation learning method based on the autoencoder network is usually superior to the similarity measurement method in terms of automatic feature learning, adaptability, and generalization ability. It is suitable for processing large-scale, high-dimensional, and complex abnormal behavior recognition tasks, especially in the unsupervised learning paradigm, and it can effectively learn the low-dimensional representation of high-dimensional data;  
(4) The advantages of self-attention mechanism. The introduction of the self-attention mechanism significantly improves the accuracy and efficiency of abnormal behavior recognition. This mechanism enhances the model's ability to capture long-distance dependencies in the sequence. By allowing the model to dynamically adjust the degree of attention to different parts of the input, key features can be extracted more effectively, promoting the abnormal behavior recognition technology to a new height.