# Person Search via Deep Integrated Networks
## 4 Experimental Results
### Performance Metrics and Experimental Results  
The pedestrian detection performance was measured by the detection rate and false alarm rates. For the detection rate, the bounding box was correctly detected if the overlapped area between the bounding box and the ground truth was larger than the pre-set threshold \(\lambda\):  
\[\textit{Detection}\ \ \ \textit{rate}=\frac{\sum_{t=1}^{N}\sum_{k=1}^{N_{C}^{t} }I[\frac{\left|C_{k}^{t}\cap D_{k}^{t}\right|}{\left|C_{k}^{t}\cup D_{k}^{t} \right|}>\lambda]}{\sum_{i=1}^{N}\sum_{j=1}^{N_{C}^{i}}G_{j}^{i}} \tag{16}\]  
where I [\(\bullet\)] is an indicator function; \(N\) is the test image number; \(N_{C}^{t}\) is the number of ground truth boxes in the \(t\)th image; \(G_{k}^{t}\) and \(D_{k}^{t}\) are the \(k\)th ground truth and the detected box in the \(t\)th image, respectively; and \(\left|\frac{G_{k}^{t}\cap D_{k}^{t}}{G_{k}^{t}\cup D_{k}^{t}}\right|\) is the IOU. If the IOU is smaller than \(\lambda\), the bounding box is incorrect, and will be shown as a false alarm. The higher the detection rate and the lower the false alarm rate, the better the detection \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experiments** & **Experiment 1** & **Experiment 2** & **Experiment 3** \\ \hline Network Configuration & Multi-class CNN & Two-class CNN & Two-class CNN \\ Training class number & 306 & 2 & 2 \\ Feature types & RGB image & Vertically concatenated RGB image & Vertically concatenated \\ Number of training & 7508 & Different pair: 87,363 Same pair: & Different pair: 87,363 Same pair: \\ pedestrian images & 29,226 & 29,226 & 29,226 \\ Test class number & 168 & 2 & 2 \\ Number of test & & 28,224 Combinations of 168 IDs & 28,224 Combinations of 168 IDs \\ pedestrian images & & (i.e., 168 \(\times\) 168 = 28,224) & (i.e., 168 \(\times\) 168 = 28,224) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: List of experimental settings for the identification module evaluation. CNN: convolutional neural network. performance. Table 3 shows the detection results using the images captured by cameras 2 and 3. The threshold was set to \(\lambda\) = 0.5 and 0.7. The detection performance was better when \(\lambda\) was set to 0.5. The performance evaluation metrics of ReID are the cumulative matching characteristic (CMC, top-K), in which a correct matching is counted if there is at least one of the top-k gallery images with the same identity as the probe image. In Experiment 1, three test sets (i.e., TSet1, TSet 2 and TSet 3) containing different numbers of images for each test subject were used. In TSet 1, for each subject (168 subjects are in Experiment 1, as shown in Table 2), one image is randomly selected from the scene images captured by camera 3 to form the gallery set, while in TSet 2, two images are randomly selected. In TSet 3, all images for each of the 168 subjects captured by camera 3 are used. Hence, it is expected that each test identifies more images in the gallery and can achieve a higher identification rate. Note that, in our work, the pedestrian images in the probe and gallery sets are detected and cropped automatically in the detection module. Table 4 lists the top, top-5, top-10 and top-20 identification rates \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Threshold \(\lambda\)** & **Precision Rate** & **Recall Rate** & **F1-Score** \\ \hline
0.5 & 0.82 & 0.52 & 0.63 \\
0.7 & 0.49 & 0.31 & 0.38 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Detection results with different IOU thresholds. Figure 6: Training and test examples in (**a**) Experiment 1, (**b**) Experiment 2 and (**c**) Experiment 3.  
with different feature vectors and distance metric. As shown, TSet 3 was better compared to TSet 1 and TSet 2, and for each subject, more images in the gallery set improved performance. To investigate the detection effects in a real-world application, instead of using the detected bounding box, a multi-class CNN was trained by the manual-labelled ground-truth (GT) bounding box.