# 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
At the same time, each sub-ensemble is composed of a specific classifier and the combination of three CNN models, providing multiple classification outputs for each video frame. To solve the problem of low efficiency of video frame block convolution, [PERSON] et al. [52] proposed a method based on a Fully Convolutional Neural Network (FCN), extracting features of all regions from the entire video frame and performing convolution and pooling operations in parallel, which can achieve a processing speed of about 370 frames per second, meeting the requirements of real-time processing. (2) Spatiotemporal feature learning. This method integrates dynamic information in the time series based on space. This is usually achieved by using optical flow data or upgrading to 3D convolution technology, aiming to capture the complex relationship between motion and time between video frames and identify abnormalities in the movement trajectories and speeds of pedestrians. Under this framework, according to the different time-perception feature fusion strategies, it is mainly divided into two implementation approaches: a two-stream CNN architecture and a three-dimensional Convolutional Neural Network (3D CNN). The two-stream Convolutional Neural Network decomposes the processing of video data into a spatial stream and a temporal stream. The spatial stream focuses on the static content of the video frame, that is, the appearance feature in each frame image. The temporal stream focuses on analyzing the temporal changes and dynamic behaviors in the video sequence and usually uses optical flow images as the input. [PERSON] et al. [53] proposed a weakly supervised learning ABDL framework. Firstly, the Faster R-CNN network is used to identify objects (such as pedestrians) in the scene; then, the large-scale optical flow histogram (HLSOF) is used to describe the behavior features of the objects and finally, the behaviors are classified through the Multi-Instance Support Vector Machine (MISVM) to distinguish normal or abnormal. [PERSON] et al. (2020) designed a two-stream Convolutional Neural Network model (TS-CNN), as shown in Figure 5. By combining the above optical flow features and trajectory information, this network can process spatiotemporal information simultaneously and improve the accuracy of abnormal behavior detection. At the same time, the Kanade-Lucas-Tomasi (KLT) tracking algorithm is used to obtain the single-frame image of the crowd movement trajectory. This strategy enables the algorithm to better handle the occlusion problem in the crowd and further enhances the recognition of abnormal behaviors through the continuity feature of the behavior. Three-dimensional CNN adds a time dimension based on the original 2D convolution. By performing convolution on the time axis, 3D CNN can capture the change patterns of behaviors over time, which is extremely crucial for identifying abnormal behaviors (such as falling, running, gathering, etc.) that need to consider the behavior sequence and time context. [PERSON] et al. (2020) proposed a method based on a Deep Spatiotemporal Convolutional Neural Network (DSTCNN), extending two-dimensional convolution to three-dimensional space and comprehensively considering the spatial features of static images and the temporal features between front and rear frames. Firstly, the video screen is divided into multiple sub-regions and spatiotemporal data samples are extracted from these sub-regions and input into DSTCNN for training and classification, achieving accurate detection and location of abnormal behaviors. At the same time, to solve the problems of network dispersion and insufficient recognition ability, [PERSON] et al. (2020) proposed the LDA-Net framework, as shown in Figure 6. It consists of a human body detection module and an abnormal detection module.