# A Review of Abnormal Crowd Behavior Recognition Technology Based on Computer Vision
## 4 Research Status of Abnormal Crowd Behavior Recognition
### Deep Learning-Based Methods
#### 4.2.2 Methods Based on Autoencoders
[64] designed the Probabilistic Memory Autoencoder Network (PMAE), integrating 3D causal convolution and time dimension shared fully connected layers in the autoencoder network to extract spatiotemporal features in video frames, while ensuring the temporal sequence of information and avoiding the leakage of future information. Based on the spatiotemporal encoder, [PERSON] et al. [65] proposed the method of Incremental Spatiotemporal Learner (ISTL) for online learning of the spatiotemporal normal behavior patterns in video surveillance streams, thereby achieving abnormal detection and location. At the same time, an active learning strategy of fuzzy aggregation is introduced to dynamically adapt to unknown or emerging normal behavior patterns in surveillance videos, allowing the system to continuously refine the understanding of normal and abnormal behaviors based on environmental changes and the acquisition of new information. (2) The method based on hidden feature representation learning. This method focuses on using an encoder to map high-dimensional input data into a low-dimensional vector space, focusing on extracting the core information of normal behavior patterns. Subsequently, the decoder attempts to reconstruct the original data. During the optimization process, it ensures that the model captures the key structure of the behavior data. The process is shown in Figure 9. Common autoencoders can be divided into Stacked Denoising Autoencoders (SDAEs) [66] and Variational Autoencoders (VAEs) [67]. SDAE extracts higher-level feature representations by combining multiple Denoising Autoencoders (DAEs) [68]. The goal of a single DAE is to reconstruct the original data on noisy data. The main principles are the following: 1 the encoder maps the noisy input data to a low-dimensional hidden representation and 2 the decoder reconstructs the original noise-free data based on this and minimizes the reconstruction error to learn effective information. During the stacking process, the output of the first DAE (i.e., the denoised  
Figure 8: Anomaly behavior recognition process based on similarity measurement. representation) serves as the input of the second DAE, and so on. Each layer attempts to further remove noise or extract more abstract features from the output of the previous layer. [PERSON] et al. [69] used two SDAEs to learn the appearance features and motion features of behaviors. For the appearance feature, the input is the image block within the spatiotemporal volume around the dense trajectory; the motion feature is based on the optical flow block. Each SDAE contains multiple encoding layers, and the number of nodes is halved layer by layer until the bottleneck layer is reached. The output of this bottleneck layer is regarded as the learned deep feature. The structure is shown in Figure 10.  
VAE is a generative model based on a probabilistic framework. Its encoder is similar to the traditional encoder, mapping the input \(x\) to a pair of vectors \(u\) and \(\sigma\), representing the mean and standard deviation of the posterior distribution \(q(z\,|\,x)\), respectively. The decoder receives the latent variable z and attempts to reconstruct the original input \(x\), that is, estimating \(p(x\,|\,z)\). The output of the decoder can be regarded as the probability distribution of \(x\) given \(z\). [PERSON] et al. [70] designed a new method called S2-VAE. This method combines the stacked fully connected variational autoencoder (SF-VAE) and the skip convolutional variational autoencoder (SC-VAE). Among them, SF-VAE is a shallow generative network designed to simulate Gaussian mixture models to adapt to the actual data distribution. Filter obvious normal samples and improve the speed of abnormal detection. SC-VAE is a deep generative network that integrates low/middle/high-level features and reduces the loss in the information transmission process by fusing the features between the encoder and decoder layers through skip connections.