# Instance Hard Triplet Loss for In-video Person Re-identification
## 4 Experiments  
### Datasets  
For the cross-camera ReID task, we used three widely-used public datasets, Market-1501 [1], DukeMTMC-reID [7], and CUHK03 [6], while for the in-video ReID task, we used a new proposed in-video ReID dataset called PoseTrack-ReID.  
Here is the introduction for the three public cross-camera ReID datasets:  
Market-1501 was collected at Tsinghua University using 6 cameras. There were 1501 identities and 32,668 bounding box images in total. Those bounding box images had a fixed size of 128 \(\times\) 64 and were generated by a Deformable Part Model (DPM) [58] pedestrian detector.  
DukeMTMC-reID is an image-based person re-identification based on the DukeMTMC dataset [8], in the format of the Market-1501 dataset. It crops pedestrian images from the videos every 120 frames, yielding in total 36,411 bounding boxes with IDs.  
CUHK03 was proposed in [6], collecting images using 10 cameras. Each identity was observed by a pair of 2 cameras, and there were 1467 identities in all. Both DPM [58] detected and manually labeled bounding boxes were provided. We used the labeled version in this paper. A new recent protocol proposed in [57] was adopted in this paper with a fixed split for which 7368 images of 767 identities were used for training and 6728 of 700 identities were used for the testing.  
Despite the fact that the above three public cross-camera datasets are widely used, they are not applicable to the in-video ReID task. To solve the in-video ReID problem, we needed a dataset to train and evaluate an in-video ReID method, which should (1) be video-based, (2) have annotations for persons appearing in the same frame, (3) be large enough, and (4) provide full images so the pedestrian detection errors can be examined. However, no existing ReID dataset satisfied those demands. Therefore, we proposed a new large-scale video-based in-video ReID dataset, called PoseTrack-ReID.  
Our PoseTrack-ReID raw images came from a pose tracking dataset, PoseTrack [59]. The original PoseTrack [59] dataset contained human keypoint annotations, but did not have the bounding box of a human body. Based on the keypoint annotations, we first removed persons with less than 6 keypoints and obtained the bounding box of all keypoints. Then, we adjusted those bounding boxes to surround the whole body. We also removed frames with less than two persons present.  
We split PoseTrack-ReID into a fixed train/val/test division with 250/37/50 videos, respectively. Statistical information and a comparison with the above three cross-camera datasets and some video-based datasets are shown in Table 1. Samples are shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name** & **\#identities** & **\#bboxes** & **Video?** & **Full Image?** \\ \hline CUHK03 [6] & 1467 & 13,164 & & \\ Market-1501 [1] & 1501 & 32,217 & & \\ DukeMTMC-reID [7] & 1812 & 36,441 & & ✓ \\ PRID2011 [60] & 934 & 24,541 & ✓ & \\ iLIDS-VID [61] & 300 & 42,495 & ✓ & \\ MARS [62] & 1261 & 1,191,003 & ✓ & \\ \hline PoseTrack-ReID & 3088 & 84,443 & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the statistical information between PoseTrack-ReID and some widely-used cross-camera ReID datasets. # is the number of the corresponding item. MTMC, Multi-Target, Multi-Camera.  
Note that the MARS [62] dataset contained more bounding boxes from a long continuous sequence, but it had fewer identities and missed full-frame images. MARS [62] contained only campus images of Tsinghua University. DukeMTMC-reID [7] also only contained campus images of Duke University. As a comparison, our PoseTrack-ReID dataset contained a large-scale of identities and bounding boxes, with full-image videos available in diverse scenes. As shown in Figure 5d, each video contained multiple persons, and the persons could be very similar to each other.  
Each video had about 31 labeled frames, and unlabeled images before and after those labeled frames were also provided for future unsupervised methods.