## 2 Related Work
### Other Techniques in Video Anomaly Detection  
Beyond ViTs, various other techniques are explored in video anomaly detection. This includes research on using neural networks, CNNs, and LSTM networks for anomaly detection in video surveillance. [PERSON] and [PERSON] [PERSON] and [PERSON] (2018) achieved a high accuracy rate in their neural network-based anomaly detection system, indicating the potential for real-time monitoring applications. [PERSON] et al. [PERSON] et al. (2018) presented a framework using CNN features and bi-directional LSTM, showing improved accuracy in benchmark datasets. Moreover, [PERSON] et al. [PERSON] et al. (2018) introduced a dual-generator generative adversarial network method for detecting anomalies by learning the anomaly distribution in advance. It employs a noise generator for creating pseudo-anomaly frames and a reconstruction generator to learn normal video frame distribution. In addition, a second-order channel attention module enhances learning capacity. Furthermore, a self-supervised predictive architectural building block was developed by [PERSON] et al. [PERSON] et al. (2018). The block has a convolutional layer that masks the central region of the receptive field using dilated filters. Then, the generated activation maps are transmitted through a channel attention module. The block has a loss function that reduces the reconstruction error for the masked region in the receptive field. To replace the conventional two-stream network, a convolutional recurrent autoencoder (CR-AE) integrating an attention-based convolutional long short-term memory (ConvLSTM) network with a convolutional autoencoder to simultaneously capture both temporal and spatial irregularities was introduced by [PERSON] and [PERSON] [58]. Another attention encoder-decoder-based framework was developed by [PERSON] et al. [59], which used the corresponding intermediate layers from the encoder and decoder to train the model simultaneously. The authors pointed out that this improved the model's ability to detect anomalies by enabling it to capture more complex characteristics. Besides, the motion feature extraction was performed using a motion loss function that uses the actual video frames instead of optical flow with a parameter-free variance attention method that concentrates attention on moving objects. Furthermore, [PERSON] et al. [28] utilized a fully convolutional encoder-decoder network with symmetric skip connections to discover the spatiotemporal relationships among objects. The authors implemented an attention mechanism during the encoding phase to improve the understanding of the spatiotemporal relationships among different object types. In addition, a dynamic pattern generator in the decoding phase was designed to memorize these relationships, thereby improving the reconstruction of normal samples and making abnormal sample reconstructions more challenging. Another encoder-decoder framework called SMAMS was proposed by [PERSON] et al. [60] and built using memory modules and a spatiotemporal masked autoencoder. It utilized spatiotemporal cues and extracted the spatiotemporal properties of the video events by using a spatiotemporal masked autoencoder. Then, memory modules were used to maintain unmasked video patches of various feature layers with skip connections to recompense for crucial information loss. A method that used parallel spatial-temporal CNNs to address the unusual distribution of information in video frames was introduced by [PERSON] et al. [61]. They utilized an optical flow algorithm combined with a varied-size cell structure to segment spatial-temporal interest blocks containing moving objects. A parallel 3D-CNN was utilized to describe the same behavior at different temporal lengths, ensuring comprehensive capture of behavior information while reducing irrelevant data. Another 3D-CNN-based work proposed by [PERSON] and [PERSON] [62] focused on both spatial and temporal data for detecting anomalies, specifically violent behaviors, in videos.