## 6 Conclusions  
In this paper, we proposed a shadow silhouette-based skeleton extraction (SSSE) method. SSSE extracts three-dimensional human skeleton based on the human shadow information on the ground. Specifically, the proposed SSSE method comprises the following:  
1. A block matrix-based projection transformation is proposed, allowing the reconstruction of precise shadow silhouette information from human shadow captured by monocular camera.
2. A silhouette shadow-based human skeleton extraction method is proposed. The proposed SSSE method extracts 3D positions of seven major joints in the human skeleton based on the reconstructed human shadow silhouette information and light source position.
3. A temporal-spatial integration algorithm for discrete shadow silhouette information is proposed, empowering the SSSE-based human skeletonization in single light source scenario.  
As shown in Table 2, compared with the traditional RGB-D human skeletonization method and other mono-RGB method, the proposed SSSE method has the following advantages:  
1. The SSSE method can be deployed in large-scale outdoor scenarios where traditional 3D human skeletonization algorithms are not effective.
2. the SSSE method is capable of extracting human skeleton from frames shot by any normal monocular camera.
3. The SSSE method can be deployed in stretching the effective range of traditional RGB-D skeletonization method in the fusion application.  
For traditional outdoor surveillance systems, the limited 8-Bit color depth in the analogy transmission system restricts the precision of depth information. Based on the proposed SSSE method, precise 3D human skeleton activities can be extracted at any monitoring terminal. The extracted 3D human skeleton activities will enrich the information for surveillance video analyses, empowering convenient 3D scenario reproduction. Because of the simplicity in device requirement and the compatibility with the traditional surveillance network, the proposed SSSE is an ideal upgrade solution for a traditional surveillance system without extra hardware expenditure.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline \multirow{2}{*}{**Methods**} & **Device** & **Effective** & **Output** & **Joint** \\  & **Requirement** & **Range \(R_{e}\)** & **Format** & **Numbers** \\ \hline SSSE & Single RGB Camera & 7.0 m \(<\)\(R_{e}\) \(<\) 10 m & Human Skeleton & 7 \\ Traditional RGB-D Method [20] & RGB-D Camera & 0.8 m \(<\)\(R_{e}\) \(<\) 35 m & Human Skeleton & 20 \\ SSSE and RGB-D Fusion & RGB-D Camera & 0.8 m \(<\)\(R_{e}\) \(<\) 10 m & Human Skeleton & 7 to 20 \\ [PERSON]’s RGB-D method [16] & RGB-D Camera & Not Available (N/A) & Human Voxel & 0 \\ [PERSON]’s mono-RGB method [6] & Multiple RGB Cameras & N/A & Partial Voxels & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison between external sensor information-based quadcopter monitoring methods.  
Figure 8: Maximum process capability test.  
In conclusion, SSSE offers an extra choice for 3D human skeletonization other than depth camera, wearable sensors, or illuminator array, laying down a milestone to deploy in-lab human skeleton-related methods [6; 16; 20] in outdoor scenarios with normal photographic devices. Based on the unique outdoor merits provided by SSSE, we will focus our future research on applications of SSSE on outdoor surveillance and unmanned aerial vehicle navigation.  
We appreciate the detailed comments and kind suggestions provided by reviewers. This research was supported by the National Natural Science Foundation of China under Grants No. 61571346 and'61305040.  
All authors contributed to the research work. [PERSON] conceived the new SSSE method and designed the experiments. [PERSON] and [PERSON] reviewed the research work. [PERSON] participated in the experiments.  
The authors declare no conflict of interest.