# Multi-Level Joint Feature Learning for Person Re-Identification
## 4 Results  
### Datasets  
In order to evaluate the performance of the MFF model, here we evaluate three datasets in the experiments, i.e., Market-1501 [33], DukeMTMC-reID [6] and CUHK03 [34]. The dataset of person re-identification is divided into Training_set, Verification_set, Query and Gallery. In our experiment, the network model is trained on the training set. Then we calculate the similarity of features extracted from Query and Gallery which is used to find similar pedestrian images of Query in Gallery. Pedestrian images of the Gallery are sorted according to the similarity of image features, as shown in Figure 5.  
Figure 4: Method of body part partitions is shown above. The feature maps extracted from the deep learning model is horizontally divided into six parts. The Market-1501 [33] dataset includes 1501 identities captured by six cameras and 32,668 detected pedestrian rectangles under six camera viewpoints. In this dataset, each pedestrian contains at least two camera viewpoints. The training set is consisted of 751 identities and each identity includes 17.2 training data on average. The test set is composed of 19,732 images of 750 identities. The pedestrian detection rectangle in the gallery is detected by DPM [35]. Here, we use mean Average Precision (mAP) to evaluate person re-identification algorithms. The DukeMTMC-reID [6] dataset consists 36,411 images of 1404 identities. With those images collected by eight cameras and each image sampled every 120 frames from the video. This dataset is composed of 16,552 training images, 2228 query images and 17,661 gallery images. Half of the identities are randomly sampled as training sets while the others as test sets. DukeMTMC-reID offers human labeled bounding boxes. The CUHK03 [34] dataset is composed of 13,614 images and 1467 identities. Each identity automatically captured by two cameras. In this dataset, bounding boxes are provided by two different ways: automatically detected which is the same as Market-1501 dataset and hand-labeled bounding boxes. Here we use two kinds of bounding boxes in this paper. In the whole experiment, we evaluate the single-query setting and adopt new test protocol proposed in [36] which is similar to Market-1501. CUHK03 is divided into a training set consisting of 756 pedestrians and a test set of 700 pedestrians in the new protocol. A randomly selected image is used as query image while the rest is used as gallery. In this way, each pedestrian gets multiple ground truths in gallery. The detailed information about these datasets is summarized in Table 2. Three widely-used person re-identification datasets contain many challenges, such as misalignment, low resolutions, viewpoints and background clusters. In addition, Figure 6 shows some image samples of the four datasets. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Release Time** & **Identities** & **Cameras** & **Crop Size** & **Label Method** \\ \hline Market-1501 & 2015 & 1501 & 6 & Vary & Hand/DPM \\ DukeMTMC-reID & 2017 & 1812 & 8 & \(128\times 64\) & Hand \\ CUHK03 & 2014 & 1467 & 10 & Vary & Hand/DPM \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The details of person re-identification dataset. Figure 5: Examples of person re-identification. The similar pedestrian images of Query are shown in blue box. For each query image, we merge the five feature vectors into one and calculate the Euclidean distance between query image and pedestrian image in gallery. We use the Euclidean distance value to rank the images. The higher the ranking, the more similar the image is to the query image. Then we arrange them in descending order according to the Euclidean distance, and use the Cumulative Match Characteristic (CMC) curve to show the performance. In terms of performance measurement, we use the Rank-1 accuracy and the mean Average Precision (mAP). Mean Average Precision (mAP) is an important evaluation indicator for person re-identification. Precision and recall are important components of mean Average Precision.