# Person Search via Deep Integrated Networks
## 3 Overview of the Deep Integrated Networks for Person Search
### Discriminative Feature Extraction via the Multi-Class Identification Network  
Figure 3 shows details of the identification network consisting of four convolutional layers with the ReLU function, three fully-connected (FC) layers, and one softmax layer. Based on the order in the recognition network, the convolution layers and FC layers were assigned a cardinal number. In contrast to the previous studies [13; 15; 20], person ReID was casted as a two-class classification and the model was trained to classify the input pair as the \"same\" or \"different\" class. However, the combination amounts of the different class are much larger than for the same class. For example, there are n distinct persons (subjects) in the training dataset, and the number of pairs for the same class is n, while there are \(n\times(n-1)\) pairs for the different class. The amount of data for the classes is unbalanced. Hence, rather than learning the features of the same or different class, in the proposed system, person ReID is viewed as a multi-class classification, and the discriminative features of different subjects were therefore learned by the network. If the network extracts two images with similar characteristics, they are in the same class with a high probability. Thus, the number of subjects in the training data set (i.e., \(n=306\), in our case) was the size of the softmax layer in the multi-class identification network.  
Most of the previous studies focused on either person detection or ReID and viewed them as separate applications [9]. For ReID, the input pair of pedestrian images in the training or test process was human-cropped and labeled before being input to the identification module. However, for practical applications, the precision of the detection result (e.g., the size of the bounding box) would affect the ReID performance. Hence, in our work, the training data for the identification network were obtained from the detection network. If the intersection of overlap (IOU) between the detected bounding box and the ground truth is larger than the threshold (i.e., 0.5, in our case), confidence is assumed to give the detected bounding box the same class label (ID) with the ground truth automatically. This is done without a manual check process. Those labelled bounding boxes are used in the identification module training process, and in the following training process, the remaining unlabelled boxes are omitted. Figure 4 shows an example of the detection result (blue color) and the ground truth (red color). Note that the output of the FC6 layer was extracted as a feature vector for the input bounding box, and the similarity between the two feature vectors was measured via the learnable distance metric.  
Figure 4: Example of the detected bounding box (blue color) and the ground truth (red color). The class label (ID) is annotated above the ground truth. The bounding box with the corresponding ID is used in the training process of the identification module if the intersection of overlap (IOU) is larger than 0.5.  
Figure 3: Identification network configuration [21].