## 3 The Proposed Framework
### Anomaly Detection
#### 3.2.2 Classifier
Future studies will also investigate the test of this method on other datasets and will improve our local dataset (CapSec) in order to generate quantitative results and use it as a regular dataset for testing abnormal event detection methods. Moreover, we will also compare our classifier with other classification methods (k-nearest neighbors (k-NN) [47], and enhanced k-NN [48]). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Folder** & **EERFL** & **EERPL** & **FA** & **EERFL** & **EERPL** \\ \hline
4 & 4.4 & 7.7 & 1 & 3.3 & 7.2 \\
7 & 24.4 & 33.3 & 5 & 18.8 & 28.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Adaptation to false alarm detection. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** \\
**(IBC)**[22] & **(MDT)**[43] & **(STC)**[9] & **[24]** & **[23]** & **Ours** \\ \hline Ped2 & 83 & 25 & 0.22 & 1.38 & 0.29 & **0.15** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Brief information about computational time of the proposed method (given in seconds per frame). **Author Contributions:** conceptualization, S.B. and H.S. ; methodology, S.B. and H.S. ; software, S.B. ; analysis, S.B., H.S., M.M.H. and N.T. ; data construction, S.B. ; writing--original draft preparation, S.B. and N.T. ; writing--review and editing, S.B., N.T., H.S. and T.W. ; supervision--project administration--funding acquisition, H.S. **Funding:** This work is supported by the French regional council of Grand-Est and the European regional development fund-FEDER. **Acknowledgments:** The authors are grateful to anonymous reviewers for their comments that considerably enhanced the quality of the paper. **Conflicts of Interest:** The authors declare no conflict of interest. ## References

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