GRID: http://personal.ie.cuhk.edu.hk/~ccloy/downloads_qmul_underground_reid.html
person re-id:
http://www.ssig.dcc.ufmg.br/reid-results/
CUHK01: 2個視角，校園環(huán)境，971人，一共1552張圖像。view A 主要捕獲人的正面和背面，view B捕獲側(cè)面。每個人有4張圖像，每個視角下有2張圖像。
CUHK02 取自5個不同的戶外camera對，共1816人。5個camera對分別有971,306,107,193,239人，大小160*60. 每個人在每個攝像機(jī)下的不同時間內(nèi)取兩張圖片。大多數(shù)人都有負(fù)重（背包，手提包，皮帶包，行李）。
CUHK03：取自5個不同的視角對，共1467個行人的14000多張圖像。
VIPeR：632人，2個戶外攝像頭，有多種姿態(tài)，視角和光照變化。每個人在每個攝像機(jī)下有一張圖像，尺度為128*48。提供的角度0度（front），45度，90度（right）,135度，180（back）
iLIDS-Vid：取自監(jiān)控航空接站大廳，從2個不相交攝像機(jī)創(chuàng)建該數(shù)據(jù)集。隨機(jī)為300個人采樣了600個視頻，每人有來自兩個視覺的一對視頻。每個視頻有23~192幀，平均73幀。相似的衣服、光照和視覺改變，復(fù)雜的背景和嚴(yán)重的遮擋，很具挑戰(zhàn)性。
iLIDS：采樣了119個人479張圖像。size：128*64。每個人平均有4個張圖像。有大的照明 改變和遮擋。
PRID 2011：提供了2個不同靜止監(jiān)控?cái)z像機(jī)下的多個人的軌跡，監(jiān)控人行航道和人行道。cam A 下385人，cam B 下749人，有200人同時出現(xiàn)在兩個視角。每個視頻有5到675幀，平均100幀。該數(shù)據(jù)集是在不擁擠的戶外場景下采集的，有相對簡單和干凈的背景，較少的遮擋。
3DPeS：收集了8個不相交的戶外攝像機(jī)，監(jiān)控校園的不同地方。不同于iLIDS和PRID，它提供了完整的監(jiān)控視頻序列：提供了6個視頻對集合，15 frame/s，分辨率704*576。一共193個行人。
Shinpuhkan:包含22000多張圖像。只包含24個行人，他們從16個攝像視覺捕獲的，提供了豐富的類內(nèi)變化信息。
GRID: The QMUL underGround Re-IDentification (GRID) dataset contains 250 pedestrian image pairs. Each pair contains two images of the same individual seen from different camera views. All images are captured from 8 disjoint camera views installed in a busy underground station. The figures beside show a snapshot of each of the camera views of the station and sample images in the dataset. The dataset is challenging due to variations of pose, colours, lighting changes; as well as poor image quality caused by low spatial resolution.
CAVIAR4REID datasethttp://www.lorisbazzani.info/caviar4reid.html
The original dataset, CAVIAR, consists of several sequences filmed in the entrance lobby of the INRIA Labs and in a shopping centre in Lisbon. We selected the shopping centre scenario, because it is a less controlled recording and also the cameras are better located (in INRIA Labs scenario, the camera is located overhead. Not a typical scenario for re-identification.). Shopping centre dataset contains 26 sequences recorded from two different points of view at the resolution of 384 X 288 pixels. It includes people walking alone, meeting with others, window shopping, entering and exiting shops. The ground truth has been used to extract the bounding box of each pedestrian. Then we manual select a total of 72 pedestrians: 50 of them with both the camera views and the remaining 22 with one camera view. For each pedestrian, we accurately selected a set of images for each camera view (where available) in order to maximize the variance with respect to resolution changes, light conditions, occlusions, and pose changes so as to make challenging the re-identification task.

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