# Person Re-Identification across Data Distributions Based on General Purpose DNN Object Detector
## 5 Experimental Results
### Results  
In order to observe how the CNN evolves, we created a series of scenarios, closely following these networks' functioning. This helped us to intervene when the networks no longer converged, or the situation of gradients exploding appeared. \begin{table}
\begin{tabular}{l} \hline \hline
**Initialization:** \\ Set parameters from PostgrgSQL database: \\ \(min\_distance=FLOAT\_MAX,number\_of\_rows,norm\_max,norm\_min\) \\ Compute \(|f|\), as being the L1-norm of new feature vector based on (27) \\ Sort vectors ascending, according to L1-norm \\ Select \(k=number\_of\_rows\) most similar feature vectors, according to L1-norm \\ for \(i=1,2,\cdots,k\) : \\ Compute the distance \(D(f,f_{i})\), based on (28) and (26) \\ Compute the distance as being the minimum distance between the new feature vector and the others \(k\) vectors, \\ based on (22). \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Algorithm designed for PostgrgSQL. During the first phase of training, we trained the neural network on 30 classes, with a database of 900 images. Figure 8 presents some of the images used in this phase. This first step is only a classification one, in order to obtain the initial weights to start training in the following stage. Table 6 presents the first results that were obtained after training all of the 40 variations of neural networks for several epochs. All of the tests executed in this first scenario are performed in some extreme and challenging conditions. While we trained our initial network on a particular distribution, using frames from RPIField database, we changed all of the conditions in this testing phase. We wanted to see how this system behaves, in a completely new environment, in which it was not trained at all. We aimed to obtain a system as general as possible, which does not only work on a trained database or in similar conditions. That is the reason why we have chosen the second database, PRW-v16.04.20 [19]. This database presents a real-life scenario, capturing a crowded location with many people and objects that partially overlap. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**NETWORK** & **LEARNING** & **MOMENTUM** & **EPOCHS TRAINED** & **mAP** & **F-Score** \\
**ID** & **RATE** & & & & \\ \hline
1 & \(10^{-4}\) & 0.90 & 10,000 & 0.540167 & 0.599489 \\  & & & 20,000 & 0.526794 & 0.58512 \\  & & & 30,000 & 0.565535 & 0.592613 \\ \hline
3 & \(10^{-6}\) & 0.90 & 10,000 & 0.348933 & 0.37761 \\  & & & 20,000 & 0.298821 & 0.460086 \\  & & & 30,000 & 0.394028 & 0.501042 \\ \hline
5 & \(10^{-4}\) & 0.91 & 10,000 & 0.482261 & 0.568995 \\  & & & 20,000 & 0.535748 & 0.578173 \\  & & & **30,000** & **0.487742** & **0.604684** \\ \hline
6 & \(10^{-5}\) & 0.91 & 10,000 & 0.353856 & 0.485571 \\  & & & 20,000 & 0.411061 & 0.504279 \\  & & & 30,000 & 0.5 & 0.577131 \\ \hline
7 & \(10^{-6}\) & 0.91 & 10,000 & 0.533333 & 0.397199 \\  & & & 20,000 & 0.46791 & 0.464961 \\  & & & 30,000 & 0.527403 & 0.479184 \\ \hline
13 & \(10^{-4}\) & 0.93 & 10,000 & 0.577577 & 0.614993 \\  & & & 20,000 & 0.551397 & 0.568058 \\  & & & 30,000 & 0.54831 & 0.587156 \\ \hline
15 & \(10^{-6}\) & 0.93 & 10,000 & 0.270189 & 0.425431 \\  & & & 20,000 & 0.324363 & 0.489367 \\  & & & 30,000 & 0.536048 & 0.49375 \\ \hline
19 & \(10^{-6}\) & 0.94 & 10,000 & 0.257972 & 0.41014 \\  & & & 20,000 & 0.300079 & 0.461632 \\  & & & 30,000 & 0.281992 & 0.43265 \\ \hline
21 & \(10^{-4}\) & 0.95 & 10,000 & 0.315037 & 0.475352 \\  & & & 20,000 & 0.329039 & 0.489486 \\  & & & 30,000 & 0.376514 & 0.538335 \\ \hline
22 & \(10^{-5}\) & 0.95 & 10,000 & 0.400224 & 0.473076 \\  & & & 20,000 & 0.529153 & 0.56472 \\  & & & 30,000 & 0.4909 & 0.544563 \\ \hline
25 & \(10^{-4}\) & 0.96 & 10,000 & 0.460402 & 0.585877 \\  & & & 20,000 & 0.438733 & 0.571622 \\  & & & 30,000 & 0.439295 & 0.5609 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Preliminary Rank-5 results obtained for mean Average Precision (mAP) and F-score by modifying the values for learning rate and momentum. All of these tests were implemented on PRW-v16.04.20 database.