# A Review of Abnormal Crowd Behavior Recognition Technology Based on Computer Vision
## 4 Research Status of Abnormal Crowd Behavior Recognition
### Deep Learning-Based Methods
#### 4.2.1 Methods Based on the Convolutional Neural Network  
The Convolutional Neural Network (CNN), due to its strong ability to process pixel data and efficient learning feature representation, has become an indispensable part of the field of computer vision, such as image classification, semantic segmentation, and target feature analysis. As a deep feed-forward neural network system, the operation process of CNN can be divided into two major steps: feature extraction and classification decision-making, and the structure is shown in Figure 4. In the field of abnormal crowd behavior recognition, the current CNN-based methods mainly focus on two core directions: spatial feature and spatiotemporal feature learning. Figure 4: The structure of CNN. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method Category** & **Design Idea** & **Advantage** & **Limitation** & **Reference** \\ \hline \multirow{4}{*}{Statistical Model} & Build the background model & Strong pattern recognition & \multirow{2}{*}{\
\begin{tabular}{c} Complex parameter \\ estimation and sensitivity \\ to initial values \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} [29; 30; 31; 32; 33] \\ \end{tabular} } \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \end{tabular}
\end{table} 
Table 3: The classification and characteristics of traditional behavior anomaly recognition methods. (1) Spatial feature. It focuses on extracting the appearance information of pedestrians from video frames to identify atypical behavior patterns in appearance, that is, appearance anomalies. Based on different video forms and annotations, abnormal crowd behavior recognition methods can be divided into three learning methods: supervised, semi-supervised, and unsupervised. Supervised abnormal behavior recognition is a strategy based on annotated datasets. It extracts pedestrian features through CNN and builds a classification model to identify abnormal patterns in the original data based on normal and abnormal behavior labels [42]. Since explicit category labels are provided during training, CNN can learn precise feature representations for specific tasks, thereby achieving a high accuracy rate for labeled abnormal behavior recognition. However, this method highly depends on the annotated datasets of abnormal behaviors, and the cumbersome manual annotation limits the development of supervised algorithms. Unsupervised/weakly supervised learning methods can use CNN to extract and analyze pedestrian feature representations without explicit abnormal behavior category labels or with only a small number of labels. This method can detect unknown or unlabeled abnormal behavior types, adapt to a wider range of behavior patterns, and improve the generalization ability of the model in practical applications. Unsupervised/weakly supervised abnormal behavior recognition is implemented based on two stages: feature extraction and abnormal recognition. In the feature extraction stage, pedestrian appearance features in the video sequence are extracted through pre-trained CNNs, commonly including VGG [43], ResNet50 [44], AlexNet [45], GoogLeNet [46], Inception [47], etc. After extracting the appearance features of pedestrians, abnormal classification is performed through abnormal behavior recognition algorithms, commonly including a one-class classifier [48], Gaussian classifier [49], Support Vector Machine (SVM) [50], etc. [PERSON] et al. [51] proposed a method of Aggregation of Ensembles (AOEs), which fused and fine-tuned the three networks of AlexNet, GoogLeNet, and VGG, especially adjusting the number of output nodes of the fully connected layer to match the binary classification task, and adopted a hierarchical fine-tuning strategy, only updating the learning rate of specific layers and keeping other layers unchanged, thereby avoiding training the network from scratch and improving the efficiency of abnormal behavior recognition.