## 5 Experimental Results and Analysis
### Experimental Result and Evaluation  
In this subdivision, trials were conducted twice to assess the performance of the designed EBP model over three standard datasets. Table 1 shows the actual human count and human detection mean accuracy by the change of the given frame data [50]. This table has five columns in which the first one indicates the sequences of given frames, the second for the actual track, the third for successful detection, the fourth for failure rate, and finally the fifth for the accuracy of the avenue dataset with 91.60% mean accuracy. Table 2 shows the actual human count and human detection mean accuracy by the change of the given frame data. This table has five columns, in which first one indicates the sequences of given frames, the second for the actual track, the third for successful detection, the fourth for the failure rate, and finally the fifth for the accuracy of the UMN dataset with 90.92% mean accuracy. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sequence No** & **Actual Track** & **Successful** & **Failure** & **Accuracy** \\ \hline
6 & 5 & 5 & 0 & 100.0 \\
12 & 5 & 5 & 0 & 100.0 \\
18 & 5 & 5 & 0 & 100.0 \\
24 & 7 & 6 & 1 & 85.71 \\
30 & 9 & 7 & 2 & 77.77 \\
36 & 9 & 8 & 1 & 88.88 \\
42 & 9 & 8 & 1 & 88.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Actual human detection and recognition accuracy over avenue dataset. Table 3 shows the actual human count and human detection mean accuracy by the change of the given frame data. This table has five columns in which first one indicates the sequences of the given frames, the second for the actual track, the third for successful detection, the fourth for the failure rate, and finally the fifth for the accuracy of the ADOC dataset with 92.84% mean accuracy. The next step is to find normal and abnormal events of the proposed EBP method with the help of the maximum entropy Markov model algorithm; Table 4 shows the confusion matrix [51; 52; 53] of the avenue dataset with 87.00% of mean accuracy and 13.00% error rate. Table 5 shows the confusion matrix of the UMN dataset with a mean accuracy rate of 89.50% and an error rate of 10.50%. Table 6 shows the confusion matrix of the ADOC data set with a mean accuracy rate of 90.50 and 9.50% error. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scene No** & **Anomaly Detection** & **Error Rate** \\ \hline
**Scene 01** & 84.00 & 16.00 \\
**Scene 02** & 87.50 & 12.50 \\
**Scene 03** & 89.50 & 10.50 \\ \hline
**Mean accuracy** & 87.00\% & 13.00\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Confusion matrix of proposed EPB method over avenue dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sequence No** & **Actual Track** & **Successful** & **Failure** & **Accuracy** \\ \hline
6 & 11 & 11 & 0 & 100.0 \\
12 & 11 & 10 & 1 & 91.66 \\
18 & 12 & 11 & 1 & 91.66 \\
24 & 12 & 11 & 1 & 91.66 \\
30 & 13 & 12 & 1 & 92.30 \\
36 & 13 & 11 & 2 & 84.61 \\
42 & 13 & 11 & 2 & 84.61 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Actual human detection and recognition accuracy over UMN dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sequence No** & **Actual Track** & **Successful** & **Failure** & **Accuracy** \\ \hline
6 & 12 & 12 & 0 & 100.0 \\
12 & 12 & 11 & 1 & 91.66 \\
18 & 13 & 12 & 1 & 92.30 \\
24 & 13 & 12 & 1 & 92.30 \\
30 & 14 & 13 & 1 & 92.85 \\
36 & 15 & 14 & 1 & 93.33 \\
42 & 16 & 14 & 2 & 87.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Actual human detection and recognition accuracy over ADOC dataset. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Scene No** & **Anomaly Detection** & **Error Rate** \\ \hline
**Scene 01** & 87.50 & 12.50 \\
**Scene 02** & 89.50 & 10.50 \\
**Scene 03** & 91.50 & 08.50 \\ \hline
**Mean accuracy** & 89.50\% & 10.50\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Confusion matrix of proposed EPB method over UMN dataset. After obtaining the results of means accuracy for the EPB system, the next phase is to compare with a state-of-the-art classification algorithm.