## 4 Experiments
### Image Feature Extraction
#### 4.2.2 Comparison between Different Integrated Feature Extraction Methods  
The proposed method was also compared against several popular integrated feature extraction methods, including discrete Fourier transform (DFT), incremental visual tracking (IVT), compressive tracking (CT), direct linear transform (DLT), etc. The mean center errors and success rates of these methods are listed in Table 5.  
As can be seen from Table 5, the proposed integrated feature extraction method outshined the other popular methods. In terms of mean center error, the proposed method achieved an accuracy 58.2% higher than the DFT, 17.3% higher than the IVT, 7.1% higher than the CT, and 3.2% than the DLT. In terms of success rate, the proposed method surpassed the DFT, IVT, CT and DLT by 2.5%, 1.7%, 0.9% and 0.4%, respectively. The across-the-board advantages arise from the complementary effect between shape and intrinsic features, the highlighting of target feature points by the SURF, and the edge of GoogLeNet over the other deep convolutional neural networks (DCNNs).  
Taking one vibration source as an example, each frame image was analyzed by the proposed method, the DFT, the IVT, the CT and the DLT. According to the results in Figure 11, the proposed method lined out the range of the target source perfectly, while the other four methods each had its biases. Thus, the proposed method is an ideal tool to extract features from the images generated from \(\Phi\)-OTDR signals.  
Figure 10: Comparison of two indexes under different weight.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Name**} & **Hammer No.** & **Hammer No.** & **Hammer No.** & **Hammer No.** \\  & & **1** & **2** & **3** & **4** & **5** \\ \hline
1 & DFT & 14.1 (88.1) & 12.3 (87.6) & 15.1 (89.9) & 13.8 (87.1) & 12.0 (89.4) \\
2 & IVT & 5.7 (89.1) & 6.9 (89.5) & 6.3 (89.1) & 5.9 (90.4) & 5.1 (89.5) \\
3 & CT & 4.9 (89.2) & 4.9 (89.1) & 5.1 (89.2) & 5.7 (92.2) & 4.9 (89.8) \\
4 & DLT & 4.7 (89.6) & 4.8 (90.8) & 5.2 (89.7) & 5.3 (92.9) & 4.4 (90.7) \\
5 & Mixed-features & 4.5 (91.1) & 4.1 (92.3) & 4.7 (90.7) & 4.9 (93.6) & 3.9 (91.5) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Mean center errors (pixel) and the success rates (%) of different methods.  
Signal damage is an unavoidable phenomenon in any data compression image. The proposed image-based approach is no exception. In light of this, several experiments were performed to measure the success rates of the proposed method and several traditional signal processing methods, and the results are shown in Table 6.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Name**} & **Hammer** & **Hammer** & **Hammer** & **Hammer** & **Hammer** \\  & & **No. 1** & **No. 2** & **No. 3** & **No. 4** & **No. 5** \\ \hline \multirow{2}{*}{1} & FFT (Fast Fourier Transform) & 95.8 & 96.5 & 95.2 & 96.7 & 96.6 \\  & HHT (Hilbert-Huang Transform) & 96.3 & 96.7 & 97.0 & 97.1 & 97.0 \\  & WT (Wavelet Transform) & 96.9 & 97.2 & 97.1 & 97.2 & 97.1 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The success rate (%) in different methods.  
Figure 11: Subjective effect of target recognition in different methods.  
The data in Table 6 demonstrate that some traditional methods could achieve over 95% success rates in source tracking. However, the success rate of the proposed method was considerably lower because our method involves not only data compression, but also feature retention. In summary, the proposed image-based approach is improved on other image-based methods, but poorer than the traditional signal processing methods.