## 3 Proposed System Methodology
### Crowd Behavior Detection
#### 3.4.1 Global-Local Descriptors
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Indoor/Outdoor Scenes** & **Force Field Model** & **Social Force Model** & **Proposed Method** \\ \hline Scene 1 & 88.69 & 84.41 & 87.43 \\ Scene 2 & 80.00 & 82.35 & 83.21 \\ Scene 3 & 77.92 & 90.83 & 90.63 \\ Overall accuracy & 81.04\% & 85.09\% & 86.06\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the proposed approach with other state-of-the-art methods for human crowd behavior detection on the UMN dataset. \begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **Counting Accuracy (\%)** \\ \hline Pixel-map based algorithm [94] & 83.6 \\ Sparsity-driven [111] & 86.3 \\ Head Shoulder based detection [100] & 86.7 \\ Skin Detection [81] & 88.7 \\ Proposed method & 89.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of proposed approach with state-of-the-art multi-person counting methods. promising results on the publicly available benchmark UMN dataset, with an accuracy of 86.06%, as shown in Table 3. Our future work will focus on some occlusion reasoning methods to further tackle the occlusion problems. We will also extend our work to multiple scene detection. We are interested in recognition of different scenes, such as sport scenes, fight scenes, robbery scenes, traffic scenes, and action scenes. Conceptualization, F.A., Y.Y.G. and K.K. methodology, F.A. and A.J. ; software, F.A. ; validation, F.A., Y.Y.G. and K.K. ; formal analysis, M.G. and K.K. ; resources, Y.Y.G., M.G. and K.K. ; writing--review and editing, F.A., A.J. and K.K. ; funding acquisition, Y.Y.G. and K.K. All authors have read and agreed to the published version of the manuscript. This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) under Grant 2018R1D1A1A02085645, by the Korea Medical Device Development Fund grant funded by the Korean government (the Ministry of Science and ICT; the Ministry of Trade, Industry and Energy; the Ministry of Health and Welfare; and the Ministry of Food and Drug Safety) (202012D05-02), and by Hanyang University Grant No. 20180000000647. Data sharing not applicable. The authors declare no conflict of interest. ## References

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