## 5 Experimental Results and Analysis
### Experimental Result and Evaluation
Figure 12 shows the detailed results of the comparison. In study [54], they designed an approach for a fine-grained abnormal behavior understanding in the crowd with the help of low-level motion descriptors, dense trajectory, and histogram of oriented tracks. In [55], [PERSON] et al. developed a legitimate descriptor that encodes differences in crowd appearance utilizing periodic estimates of grey level co-occurrence tensor information to explain crowd behavior. As compared with other types of crowd behavior, they add an inter-frame consistency indicator and show the perception of aggressive behavior in a less consistent manner. [PERSON] et al. [56] designed a model to check abnormal activities; the model utilizes an intelligent system that examines the compressed feature set using Gaussian Mixture Models, Markov Chains, and Bag-of-Words. To improve recognition rate, their system additionally incorporates the coordinated approach of the classifiers in the local spatial region. In [57], [PERSON] et al. used multilayer models including both luminance and movement information to propose a model for efficient abnormal identification. The architecture was made up of three primary parts: (1) denoising auto encoders for transfer learning, (2) conditional generative adversarial networks for level-wise representative production, and (3) consolidation of abnormal areas found at every representing standard. [PERSON] et al. [58] proposed to integrate a unit into the decoder and create a better encoder dubbed MemAE (memory-augmented auto encoder). MemAE collects the encoder's encoding and then utilizes it as a request to identify the most appropriate storage objects for restoration considering an argument. In [59] [PERSON] et al. suggest a solution that incorporates the benefits of both approaches while balancing their drawbacks. An edge framework was set up to forecast and reassemble upcoming frames sequentially. The next frame estimation increases the size of the restoration mistakes, making it easier to spot unusual occurrences, while restoration enhances the future expected blocks with normal events. [PERSON] et al. [60] provide a dynamical structure for detecting anomalies in cluttered scenes in video content. The framework was made up of two primary elements: one for \begin{table}
\begin{tabular}{c c c} \hline \hline
**Scene No** & **Anomaly Detection** & **Error Rate** \\ \hline
**Scene 01** & 86.50 & 13.50 \\
**Scene 02** & 91.50 & 8.50 \\
**Scene 03** & 93.50 & 6.50 \\ \hline
**Mean accuracy** & 90.50\% & 9.50\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Confusion matrix of proposed EPB method over A Day on Campus (ADOC) data set. Figure 12: Comparison of MEMM with Adaboost and K-ary Tree hashing algorithms. representing spatial data and the other for understanding the time dynamics of those qualities. For understanding and simulating the relationship of normal objects, [PERSON] et al. [61] present Ano-Graph, a unique yet fast technique. A Spatial-Temporal Graph (STG) is created throughout this manner by taking each cluster as an entity's attribute collected from a real-time off-the-shelf detection algorithm, and connections are created to interact. Following that, the STG is subjected to an identity learning system that captures events in a subspace. Table 7 shows the comparison of proposed method accuracy with state-of-the-art methods over the Avenue, UMN, and ADOC datasets.