# Parts Semantic Segmentation Aware Representation Learning for Person Re-Identification
## 4 Experiment
### Person Re-Identification Performance  
We performed experiments on three public person re-identification datasets: Market-1501 [8], CUHK03 [17], and DukeMTMC-reID [9]. **Market-1501** dataset [8] consists of images of 1,501 persons 32,668 images which cropped with bounding-boxes predicted by DPM detector [41]. These images are captured from 6 different cameras, including 5 high-resolution cameras, and one low-resolution camera. Overlap exists among different \begin{table}
\begin{tabular}{c c c c} \hline
**Method** & **Overall Accuracy** & **Mean Accuracy** & **Mean IoU** \\ \hline SegNet [37] & 0.6904 & 0.2400 & 0.1817 \\ FCN-8s [38] & 0.7606 & 0.3675 & 0.2829 \\ DeepLabV2 [39] & 0.8266 & 0.5167 & 0.4164 \\ Attention [40] & 0.8343 & 0.5255 & 0.4244 \\ DeepLabV2 + SSL [36] & 0.8316 & 0.5255 & 0.4244 \\ Attention + SSL [36] & 0.8436 & 0.5494 & 0.4473 \\ U\_Net [12] & 0.8499 & 0.5625 & 0.4677 \\ U\_Net (ours) & **0.8522** & **0.5651** & **0.4712** \\ \hline \end{tabular}
\end{table} 
Table 2: Performance of human semantic segmentation on the validation split of LIP. Figure 5: Examples of part semantic segmentation maps and corresponding bounding boxes of parts. The top row images are RGB images of person, and the bottom images illustrate their part segmentation by super-pixels and bounding boxes of human parts are demonstrated with red rectangles. cameras. The whole dataset is divided into training set with 12,936 images of 751 persons and testing set with 3368 query images and 19,732 gallery images of 750 persons. The **CHUK03** dataset [17] includes 13,164 images of 1,360 people captured by six cameras. Each identity appears in two disjoint camera views (i.e., 4.8 images in each view on average). Our dataset is partitioned into training set (1160 persons), validation set (100 persons), and test set (100 persons). The **DukeMTMC-reID** dataset [9] consists of 1,404 identities appearing in more than two cameras and 408 identities (distractor ID) who appear in only one camera. There are 16,522 training images of 702 identities, 2,228 query images of the other 702 identities and 17,661 gallery images (702 ID + 408 distractor ID). Our proposed method is compared with current state-of-the-art methods, including IDLA [23], Part-Aligned [25], PersonNet [26], HydraPlus-Net [27], BoW+kissme [8], Basel. + LSRO [9], DCSL [42], PDC [5], PSE [11], SVDNet [43], PAN [44] and ATWL [45] to show our considerable performance advantage over all the existing competitors. In our experiments, we report the cumulative matching characteristics (CMC) rank-1, rank-5, rank-10 and mean average precision (mAP) to evaluate the performances of person re-identification methods. And we use state-of-the-art network architectures, such as VGG [46], ResNet [33], DenseNet [47] and Inception v3 network [48], as our baseline network to test the performance of different networks in our approach. The results are summarized in Tables 3-5, where we denote our method as PADP (Parts Alignments with DropParts). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Rank-1** & **Rank-5** & **Rank-10** \\ \hline IDLA [23] & 0.5474 & 0.8650 & 0.9388 \\ PersonNet [26] & 0.6480 & 0.8940 & 0.9492 \\ DCSL [42] & 0.8020 & 0.9773 & 0.9917 \\ SVDNet [43] & 0.8180 & 0.9520 & 0.9720 \\ Basel. + LSRO [9] & 0.8460 & 0.9760 & 0.9890 \\ Part-Aligned [25] & 0.8540 & 0.9760 & 0.9940 \\ PDC [5] & 0.8870 & 0.9861 & 0.9924 \\ \hline PADP (VGG19+BN) & 0.9029 & 0.9879 & 0.9943 \\ PADP (Resnet18) & 0.8923 & 0.9820 & 0.9923 \\ PADP (Resnet34) & **0.9083** & **0.9896** & **0.9953** \\ PADP (Resnet50) & 0.9001 & **0.9869** & 0.9951 \\ PADP (Desnet121) & 0.9021 & 0.9842 & 0.9941 \\ PADP (Inception\_v3) & 0.9053 & 0.9849 & 0.9944 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Rank-1, rank-5 and rank-10 of various methods on the CUHK03 dataset.