# A Review of Abnormal Crowd Behavior Recognition Technology Based on Computer Vision
## 4 Research Status of Abnormal Crowd Behavior Recognition
### Deep Learning-Based Methods
#### 4.2.5 Methods Based on the Self-Attention Mechanism
[84] introduced the self-attention mechanism in the Generative Adversarial Network (GAN), using the autoencoder containing the dense residual network and the self-attention mechanism as the generator. At the same time, a new discriminator is proposed, which integrates the self-attention module based on the relative discriminator, thereby avoiding the problem of gradient vanishing during the training process and ensuring that the frames generated by the generator are closer to the real frames. [PERSON] et al. [85] proposed an Attention-guided Generator and Dual Discriminator Adversarial Network (A2D-GAN), the structure if which is shown in Figure 18, for real-time video anomaly detection. A2D-GAN utilizes the encoder-decoder architecture, in which the encoder adds multi-level self-attention to focus on key regions and capture context information, while the decoder uses channel attention to emphasize important features and identify abnormal patterns specific to each frame. In addition, to solve problems such as scale changes and occlusions in the crowd, fusion models such as multi-scale feature attention fusion networks and visual occlusion resolution have emerged for abnormal crowd behavior detection. For example, [PERSON] et al. [86] introduced a scale-aware attention module in CNN, using the cascade of multiple self-attention branches to enhance the recognition ability for scale changes. This architecture integrates motion information, motion influence maps, and features based on the energy level distribution to achieve frame-level abnormal behavior analysis. [PERSON] et al. [87] proposed a Dynamic Pedestrian Centroid Model (DCM). The key points of the human skeleton are extracted from the image, the pedestrian joint sub-segments are constructed accordingly, and the dynamic characteristics of pedestrians in the video sequence are described mathematically. Experiments show that DCM can detect the U-turn behavior 277 ms earlier on average and detect the fall behavior 562 ms earlier on average to find abnormalities. At the same time, to solve the problem of partial occlusion of pedestrians, this paper also designed an anti-occlusion algorithm, as shown in the schematic in \[Sim(Query,key_{i})=Query\bullet Key_{i}\]1 Firstly, cluster the key points of the pedestrian skeleton, divide the 21 key points into the head, upper body, and lower body, calculate the coordinates of the occluded part, estimate the effective position combined with the mechanical principle, and apply it in combination with DCM. This method has been verified by the fall behavior detection experiment and  
\[Sim(Query,key_{i})=Query\bullet Key_{i}\]2 The structure of SABiAE. Note, SABiAE is composed of non-local encoder, non-local bidirectional long short-term memory network and decoder. the AUC on the 50 Volu. The U-turn dataset reaches 82.13%, and the AUC on the 50 Volu. The fall-down dataset reaches 86.70%. In summary, Table 4 summarizes the design ideas, advantages, and disadvantages of the above various deep learning-based abnormal behavior recognition technologies. Overall, these deep learning-based methods benefit from the powerful learning ability of neural networks and can be applied to the recognition of abnormal behaviors of pedestrians in various crowd scenarios. At the same time, a strategy of using multiple methods in combination can be adopted to further improve the generalization ability and recognition accuracy of the model. \[Sim(Query,key_{i})=Query\bullet Key_{i}\]3 Diagram of the algorithm for occlusion removal based on skeleton points. \[Sim(Query,key_{i})=Query\bullet Key_{i}\]4 The structure of A2D-GAN.