You are provided with a research question, keyphrases about the question, a description of candidate datasets and dataset options. Read the description of popular datasets provided below and select the ones that can be used to validate the following research question. Use your knowledge of machine learning datasets to make the best judgement.
Your response should be formatted as a `json` array. For instance, for the query "Semi supervised image classification", a sample response might be: ["CIFAR-10", "CIFAR-100"]. Do not include any extra text in the response other than the answer array.

Query: I want to train a supervised model for person re-identification in the wild

Keyphrases: person re-identification wild video

Dataset description:
Airport: The Airport dataset is a dataset for person re-identification which consists of 39,902 images and 9,651 identities across six cameras.
ATRW: The ATRW Dataset contains over 8,000 video clips from 92 Amur tigers, with bounding box, pose keypoint, and tiger identity annotations.
Market-1501: Market-1501 is a large-scale public benchmark dataset for person re-identification. It contains 1501 identities which are captured by six different cameras, and 32,668 pedestrian image bounding-boxes obtained using the Deformable Part Models pedestrian detector.
DukeMTMC-reID: The DukeMTMC-reID (Duke Multi-Tracking Multi-Camera ReIDentification) dataset is a subset of the DukeMTMC for image-based person re-ID. The dataset is created from high-resolution videos from 8 different cameras.
VeRi-Wild: Veri-Wild is the largest vehicle re-identification dataset (as of CVPR 2019). The dataset is captured from a large CCTV surveillance system consisting of 174 cameras across one month (30× 24h) under unconstrained scenarios.
KITTI: KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) is one of the most popular datasets for use in mobile robotics and autonomous driving. It consists of hours of traffic scenarios recorded with a variety of sensor modalities, including high-resolution RGB, grayscale stereo cameras, and a 3D laser scanner.
SYSU-MM01: The SYSU-MM01 is a dataset collected for the Visible-Infrared Re-identification problem. The images in the dataset were obtained from 491 different persons by recording them using 4 RGB and 2 infrared cameras.

Options:- A: Airport  B: ATRW  C: Market-1501  D: DukeMTMC-reID  E: VeRi-Wild  F: KITTI  G: SYSU-MM01
["Market-1501", "KITTI"]