# Three-Stage Deep Learning Framework for Video Surveillance
## 2 Related Works  
Currently, research on anomaly detection in CCTV videos is being actively conducted using various techniques. In order to compare and analyze the proposed architecture, we investigated various methodologies and confirmed their performance. In this section, we introduce diverse methodologies. First, [PERSON], [PERSON] [14] proposed an extension of Improved Fisher Vectors (IFV) for videos, which enables the spatiotemporal localization of features to increase accuracy. Additionally, they re-formalized IFV and proposed a sliding window approach that utilizes aggregated region table data structures for violence detection. Subsequent advancements in technology have enabled more sophisticated analysis. Some techniques utilize optical flow [15; 16; 17; 18] before training the network. Optical flow refers to the visible motion pattern of an object captured in consecutive video frames. When the movement of an object occurs in an image, it is projected onto a 2D image space, where a myriad of vectors from the 3D space can be projected as vectors in the 2D image space because the dimensions are reduced. Examples of these advanced techniques include the following. Skeleton-based anomaly detection utilizes graph convolutional networks (GCNs). A GCN predicts the label of each node in a given input graph and updates its hidden state using Equation (1), where \(H\) represents the hidden state matrix of the 1 th hidden layer, \(A\) represents the adjacency matrix with self-connections and an identity matrix, and \(W\) represents the weight matrix. \[H^{(l+1)}=\sigma(AH^{l}W^{l}+b^{l}), \tag{1}\]  
Skeleton-based anomaly detection [19] converts human images into graphs and analyzes the interactions within these graphs to detect anomalies. For example, [PERSON] [20] proposed an architecture using human pose extractors and ConvLSTM. The author relied on what they considered to be the most essential information to detect human bodies and their interactions, using human pose extractors and optical flow for this purpose. The architecture consists of RGB and motion detection pipelines, each of which analyzes image distributions and skeletal movements. [PERSON], [PERSON] [21] proposed an architecture that learns inter actions between skeletal points using 3D skeleton point clouds. They aimed to represent videos as human skeleton point clouds using the multi-head Skeleton Points Interaction Learning (SPIL) module and perform inference for violence recognition in videos. There are also studies on anomaly detection techniques using Conv3D. Typically, CNNs apply operations to images using two-dimensional (2D) kernels. However, this method can only be applied to static images. Meanwhile, CCTV image analysis includes both still images and the analysis of temporal data, making it impossible to perform analysis using a generic CNN. Therefore, the passage of time should be incorporated into the 2D CNN, and a Conv3D network has been developed for this purpose. Conv3D takes inputs in three dimensions, and its kernel is also three-dimensional (3D). The rest of the computation is the same as that of a conventional 2D CNN; however, the direction of movement of the kernel occurs along the x-, y-, and z-axes, and the convolution operation is applied to convert the \(n\times n\times n\) 3D inputs into a single output. In previous studies, several techniques have incorporated optical flow or attention modules [22]. For instance, [PERSON] [23] proposed a flow-gated network that leveraged both 3D-CNN and optical flow. Video frames were divided into RGB and optical flow channels, each processed through Conv3D networks. The outputs from the last layers of the RGB and optical flow channels underwent ReLU and sigmoid operations before concatenation. The resulting output was used for anomaly detection. [PERSON] [4] used multiple instance learning (MIL) and 3D convolution to differentiate between normal and abnormal scenarios. The author separated normal and abnormal video clips into positive and negative bags, respectively.