## 2 Related Works
The suggested scheme was able to capture the discriminative spatiotemporal features effectively. Ref. [16] proposed a descriptor that analyse the crowd dynamics in real time by observing the crowd texture changes. GLCM features were used for temporal summaries. The authors also proposed the interframe uniformity (IFU), which explained the change in violent behaviour compared to normal crowd behaviour changes. The proposed method used the Haralick texture features obtained from each frame of the sequences and described the involvement of these features in crowd dynamics. It also emphasized that violent behaviour usually had a less uniform rate of changes over time than normal crowd behaviour. Ref. [17] used an autoencoder to detect anomalies in crowded videos. After a training process, the autoencoder identified the complicated distribution of the normal dynamics of behaviour change. It used state-of-the-art hand-crafted HOG and HOF motion features models with enhanced trajectory features as an autoencoder input. The autoencoder learned the regular motion signatures by using a fully connected neural network. As these motion features could not be ideal for anomaly detection, the model learned the motion features and trained the encoder using an end-to-end learning version of a fully connected CNN during training. The learned model was analysed by visualizing the consistency in frames also predicting a normal past and future frame utilizing a single image. Ref. [18] proposed a convolutional LSTM built on an autoencoder-based approach for anomaly detection in videos. With the help of a CNN, the authors characterized each frame's content in the video, and ConvLSTM was used to characterize the motion information. Moreover, ConvLSTM preserved the spatial information, which helped the reconstruction of frames. Ref. [19] proposed MemAE, a memory-augmented autoencoder for anomaly detection. The model consisted of three units: an encoder, a decoder and a memory unit. Ref. [20] used a CNN model for feature extraction and context analysis from surveillance videos. Then, it used a denoising autoencoder to deliver precise surveillance anomaly detection. To construct the background segmentation features, it used a panoptic feature pyramid network (PFPN). The PFPN model was also used to solve unified tasks on amorphous background regions such as rivers, walls, etc. The proposed model used the joint detection and embedding (JDE) model for pedestrian detection and tracking features. Ref. [21] suggested an unsupervised deep learning framework to analyse the anomalies in crowded videos. The handcrafted features included energy features, low-level visual features and a motion map, and these features were used for anomaly detection in a video. The proposed scheme consisted of three stages. Firstly, three CDBNs were used to learn the midlevel features from crowd data. Secondly, these midlevel features were merged using a fusion scheme to form a deep high-level representation. Finally, a one-class support vector machine (SVM) was used to learn from the deep representation. Based on this learning, the SVM detected any abnormal data existing in crowd videos. Ref. [22] used a deep cascade autoencoder (CDA) to detect any abnormal event in surveillance videos. The moment information was captured by a motion descriptor, namely a multiframe descriptor (MHOFO). Then, these features extracted by a descriptor were fed to the CDA network for the training process. After training, the abnormal events were distinguished from the normal ones using the error rate of the CDA. Ref. [23] proposed a novel approach named aggregation of ensembles for abnormality recognition in surveillance videos. The scheme used pretrained networks, including AlexNet, GoogleNet and VGGNet, based on the theory that these networks learn different semantic representation levels from crowd videos. The proposed AOE used various pre-trained networks for feature extraction and trained an SVM classifier on these features to predict anomalies in crowd scenes. Ref.