# Three-Stage Deep Learning Framework for Video Surveillance
## 2 Related Works
Features were extracted using 3D convolution, and a multiple instance ranking objective function was proposed to rank the two instances with the highest anomaly scores within each bag. [PERSON] [24] introduced an approach to detect abnormal events in surveillance videos using a recurrent learning framework, a random forest ensemble, and novel terms for score propagation. The author used a weakly supervised network model to classify videos into bags with positive and negative instances and detected abnormal situations in unlabeled data using 3D convolution and a Bayesian classifier. [PERSON] [2] utilized a Gaussian Mixture Model (GMM) composed of a soft clustering technique to connect data points to clusters. The author introduced a new MIL solution with a loss function that incorporated both a two-kernel GMM and the estimated parameters of normal distributions. Features were extracted using 3D convolution. [PERSON] [25] improved the model accuracy by adding hard attention to semi-supervised learning, utilizing the optical flow extracted from input data, and applying 3D convolution. There are also studies on anomaly detection techniques using ConvLSTM. To understand ConvLSTM [26], we must first examine long short-term memory (LSTM) [27]. In conventional RNNs, when long intervals of data are input, backpropagation requires a long time, and slope loss occurs. To prevent this, an LSTM with a state store, an input gate, and an output gate was developed. LSTM is faster than conventional methods because each gate unit is trained to appropriately open and close the flow, which solves the slope loss problem. ConvLSTM introduces convolutional recurrent cells into the basic LSTM structure and applies convolutional operations to the images as inputs. Consequently, it can effectively learn the spatial characteristics of the image. Moreover, because it has the characteristics of LSTM, it can also learn the temporal continuity among image frames. Utilizing these characteristics, research has been conducted on anomaly detection techniques using ConvLSTM [28]. For example, [PERSON] [29] proposes an efficient two-stream deep learning architecture. The architecture leverages separable convolution LSTM (sepconvlstm) and a pre-trained MobileNet. One stream takes suppressed frames as input to model the background, while the other stream processes the differences between adjacent frames. Additionally, the author presents three fusion methods to combine the output feature maps of the two streams. [PERSON] [30] introduces a combined architecture of 2D convolution and ConvLSTM. Features are extracted frame-wise using a pre-trainedAlexNet, followed by convlstm layers with 256 filters, batch normalization, and ReLU activation functions to detect anomalies. Additionally, there are anomaly detection techniques based on Transformers, which serve as the foundation for the present study. [PERSON] [31] conducted anomaly detection using Videoswin Transformer feature extraction, attention layers, and the Robust Temporal Feature Magnitude (RTFM). Furthermore, [PERSON] [32] proposed a new model called ANomaly Detection with Transformers (ANDT) to detect abnormal events in aerial videos. In this approach, videos are processed into a sequence of tubes, and a Transformer encoder is employed to learn spatiotemporal features. Subsequently, the decoder combines with the encoder to predict the next frame based on the learned spatiotemporal representation. [PERSON] [33] provides a hierarchical GVAED taxonomy that systematically organizes the existing literature by supervision, input data, and network structure, focusing on the recent advances, such as weakly supervised, fully unsupervised, and multimodal methods. Furthermore, focal loss [34] is employed as part of the network architecture.