先看看官方文檔：然后附上蘇的博客鏈接http://bigbrothersue.com/index.php/2017/12/20/person-re-id/
The Market-1501 dataset is collected in front of a supermarket in Tsinghua University. A total of six cameras are used, including 5 high-resolution cameras, and one low-resolution camera. （5個高像素，1個低像素）Overlap exists among different cameras. Overall, this dataset contains 32,668 annotated bounding boxes of 1,501 identities. In this open system, images of each identity are captured by at most six cameras. We make sure that each annotated identity is present in at least two cameras, so that cross-camera search can be performed. The Market-1501 dataset has three featured properties:
First, our dataset employes Deformable Part Model (DPM) as pedestrian detector.
Second, in addition to the true positive bounding boxes, we also provde false alarm detection results.
Third, each identify may have multiple images under each camera. During cross-camera search, there are multiple queries and multiple ground truths for each identity.
The Market-1501 dataset is annotated using the following rules. For each detected bounding box to be annotated, we manually draw a ground truth bounding box that contains the pedestrian. Then, for the detected and hand-drawn bounding boxes, we calculate the ratio of the overlapping area to the union area. If the ratio is larger than 50%, the DPM bounding box is marked as “good”; if the ratio is smaller than 20%, the bounding boxe is marked as “distractor”; otherwise, it is marked as “junk”, meaning that this image is of zero influence to the re-identification accuracy.
The package contains four folders.
1) “bounding_box_test”. There are 19,732 images in this folder used for testing.
2) “bounding_box_train”. There are 12,936 images in this folder used for training.
3) “query”. There are 750 identities. We randomly select one query image for each camera. So the maximum number of query images is 6 for an identity. In total, there are 3,368 query images in this folder.
4) “gt_query”. This folder contains the ground truth annotations. For each query, the relevant images are marked as “good” or “junk”. “junk” has zero impact on search accuracy. “junk” images also include those in the same camera with the query.
5) “gt_bbox”. We also provide the hand-drawn（這個是人工畫出的bbox） bounding boxes. They are used to judge whether a DPM bounding box is good.
Naming Rule of the bboxes
In bbox “0001_c1s1_001051_00.jpg”, “c1” is the first camera (there are totally 6 cameras).
“s1” is sequence 1 of camera 1. Here, a sequence was defined automatically by the camera. We suppose that the camera cannot store a whole video that is quite large, so it splits the video into equally large sequences. Two sequences, namely, “c1s1” and “c2s1” do not happen exactly at the same time. This is mainly because the starting time of the 6 cameras are not exactly the same (it takes time to turn on them). But, “c1s1” and “c2s1” are roughly at the same time period.
“001051” is the 1051th frame in the sequence “c1s1”. The frame rate is 25 frames per sec.
As with the last two digits, remember we use the DPM detector. Then, for identity “0001”, there may be multiple detected bounding boxes in the frame “c1s1_001051”. In other words, a pedestrian in the image may have several bboxes by DPM. So, “00” means that this bounding box is the first one among the several.
0002 ———->每一個人的獨特標簽，如上三個人是同一個人，所以標簽值相同。
這是另一個人，標簽值為0023，不同。
c1s1 ———–>是camera1 sequence1的縮寫，共有c1,c2,c3,c4,c5,c6六個攝像機，每個攝像機又有數個的錄像段， 這里是攝像機一的 第 一個錄像段
000451 ————>是c1s1的第000451幀圖片
03 ————>每一幀可能會框出好幾個這樣的bboxes，所以這是這一幀上第三個框。