## 2 Extreme Learning Machine (ELM)-Based Moving Cast Shadow Detection
### Moving Cast-Shadow Detection Algorithm
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Scene**} & \multirow{2}{*}{**Metric**} & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **Proposed** \\ \cline{2-13}  & & **[**21**]** & **[**20**]** & **[**32**]** & **[**29**]** & **[**31**]** & **[**26**]** & **[**27**]** & **[**9**]** & **[**22**]** & **[**30**]** & **[**22**]** & **[**30**]** & **[**30**]** \\ \hline \multirow{3}{*}{Campus} & \(\eta\) & 74.59 & 77.74 & 13.90 & 89.39 & 74.30 & 78.16 & 84.36 & 82.20 & 77.64 & — & 79.35 \\ \cline{2-13}  & \(\xi\) & 77.60 & 83.39 & 86.58 & 20.67 & 84.90 & 80.11 & 78.95 & 75.10 & 91.83 & — & 96.22 \\ \cline{2-13}  & \(Avg\) & 76.10 & 80.56 & 50.24 & 55.03 & 79.60 & 79.14 & 81.66 & 78.65 & 84.73 & — & 87.79 \\ \hline \multirow{3}{*}{CAVIAR} & \(\eta\) & 91.57 & 93.77 & 30.80 & 69.62 & 92.49 & 93.69 & 94.76 & — & 94.91 & — & 97.28 \\ \cline{2-13}  & \(\xi\) & 86.76 & 82.07 & 78.75 & 23.45 & 99.26 & 87.72 & 81.15 & — & 94.55 & — & 98.34 \\ \cline{2-13}  & \(Avg\) & 89.16 & 87.92 & 54.78 & 46.54 & 95.87 & 90.71 & 87.96 & — & 94.73 & — & 97.81 \\ \hline \multirow{3}{*}{Hallway} & \(\eta\) & 90.43 & 95.18 & 91.05 & 91.51 & 83.45 & 95.20 & 95.16 & 91.30 & 94.85 & — & 95.83 \\ \cline{2-13}  & \(\xi\) & 77.06 & 95.73 & 19.76 & 19.09 & 99.25 & 97.97 & 95.94 & 91.40 & 98.79 & — & 98.01 \\ \cline{2-13}  & \(Avg\) & 83.74 & 95.46 & 55.41 & 55.30 & 91.35 & 96.59 & 95.55 & 91.35 & 96.82 & — & 96.92 \\ \hline \multirow{3}{*}{Highway} & \(\eta\) & 72.00 & 81.84 & 60.54 & _67.69_ & 73.75 & 65.69 & 87.89 & 89.50 & 84.62 & 83.44 & 88.24 \\ \cline{2-13}  & \(\xi\) & 95.08 & 94.26 & 75.56 & 75.06 & 97.55 & 91.88 & 94.34 & 70.10 & 93.63 & 89.76 & 94.66 \\ \cline{2-13}  & \(Avg\) & 83.54 & 88.05 & 68.05 & 71.38 & 85.65 & 78.79 & 91.11 & 79.80 & 89.13 & 86.60 & 91.45 \\ \hline \multirow{3}{*}{Laboratory} & \(\eta\) & 67.94 & 85.99 & 17.18 & 86.43 & 89.17 & 85.83 & 92.50 & 82.90 & 87.46 & — & 92.72 \\ \cline{2-13}  & \(\xi\) & 73.69 & 96.65 & 81.73 & 50.07 & 92.66 & 94.96 & 93.23 & 88.50 & 95.28 & — & 92.73 \\ \cline{2-13}  & \(Avg\) & 70.82 & 91.32 & 49.45 & 68.25 & 90.91 & 90.39 & 92.86 & 85.70 & 91.37 & — & 92.72 \\ \hline \multirow{3}{*}{Intelligent Room} & \(\eta\) & 72.86 & 91.36 & 16.14 & 72.38 & 84.37 & 93.99 & 92.47 & 79.50 & 91.13 & 81.23 & 96.65 \\ \cline{2-13}  & \(\xi\) & 97.48 & 94.50 & 88.17 & 80.88 & 94.27 & 92.16 & 93.47 & 88.70 & 96.92 & 85.12 & 95.26 \\ \cline{2-13}  & \(Avg\) & 85.17 & 92.93 & 52.16 & 76.63 & 89.32 & 93.07 & 92.97 & 84.10 & 94.02 & 83.18 & 95.95 \\ \hline \multirow{3}{*}{Average} & \(\eta\) & 68.78 & 87.65 & 38.27 & 79.50 & 82.92 & 85.43 & 91.19 & 85.08 & 88.44 & 82.33 & 91.68 \\ \cline{2-13}  & \(\xi\) & 84.61 & 91.10 & 71.76 & 44.87 & 94.65 & 90.80 & 89.51 & 82.76 & 95.17 & 86.47 & 95.87 \\ \cline{1-1} \cline{2-13}  & \(Avg\) & 81.42 & 89.37 & 55.02 & 62.19 & 88.78 & 88.12 & 90.35 & 83.92 & 91.80 & 84.89 & 93.77 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Shadow detection results of the proposed method and compared methods on the popular dataset. ### Comparisons on a Large Dataset

Table 4 illustrates the shadow detection comparison results of the proposed method and eight representative advanced methods intuitively. Most of the quantitative results of comparative methods refer to original literature [22] and some are provided by other authors [24; 26]. In terms of shadow detection rate \(\eta\), the proposed method achieves the highest accuracy on PeopleInShade, Cubicle, Senoon and Sepm, while it is worse than the method proposed by [PERSON] et al. [24] on BusStation, Bungalows and Seam about 5.74%, 1.40% and 1.82%, respectively. From the aspect of shadow discrimination rate \(\xi\), the proposed method has the best capability of separating moving shadows from moving objects on most scenes except Bungalows. For the sake of comprehensively evaluating the performance, the mean metric \(Avg\) is calculated for comparison.